add application file

.gitattributes copy

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+__pycache__/
+.cache/
+openai_api.key
+hf_token.key
+core
+env/

.pre-commit-config.yaml

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+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.2.0
+    hooks:
+    -   id: trailing-whitespace
+    -   id: end-of-file-fixer
+    -   id: check-yaml
+    -   id: check-added-large-files
+-   repo: https://github.com/pre-commit/mirrors-prettier
+    rev: v3.0.1  # Use the sha / tag you want to point at
+    hooks:
+    -   id: prettier
+        types_or: [html]
+-   repo: https://github.com/psf/black
+    rev: 22.12.0
+    hooks:
+    -   id: black
+        args: [--line-length=79]
+-   repo: https://github.com/pycqa/isort
+    rev: 5.12.0
+    hooks:
+    -   id: isort
+        args: ["--profile", "black", --line-length=72]
+-   repo: https://github.com/kynan/nbstripout
+    rev: 0.6.0
+    hooks:
+      - id: nbstripout

README copy.md

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+---
+title: Sotopia Space
+emoji: 💻
+colorFrom: green
+colorTo: pink
+sdk: gradio
+sdk_version: 4.25.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+This is a synced repository with a Huggingface Space for the Sotopia project [space](https://huggingface.co/spaces/wdplx/Sotopia-demo)
+
+## Getting Started
+
+```bash
+conda create -n sotopia-space python=3.11; conda activate sotopia-space
+python -m pip install -r requirements.txt
+```
+
+To run the app, run the following command:
+
+```bash
+bash start_app.sh
+```

app.py

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+import os
+import argparse
+# from typing import Literal
+
+import gradio as gr # type: ignore
+# from sotopia_space.chat import chat_introduction, chat_tab, get_sotopia_profiles
+from sotopia_space import benchmark
+from ui_constants import CITATION_TEXT, BANNER
+
+
+# OPENAI_KEY_FILE="./openai_api.key"
+# if os.path.exists(OPENAI_KEY_FILE):
+#     with open(OPENAI_KEY_FILE, "r") as f:
+#         os.environ["OPENAI_API_KEY"] = f.read().strip()
+
+# with open("./sotopia_space/_header.md", "r") as f:
+#     HEADER_MD = f.read()
+
+def navigation_bar():
+    with gr.Column(scale=2):
+        toggle_dark = gr.Button(value="Toggle Dark") 
+    toggle_dark.click(
+        None,
+        js="""
+        () => {
+            if (document.body.classList.contains('dark')) {
+            document.body.classList.remove('dark');
+            document.querySelector('gradio-app').style.backgroundColor = 'var(--color-background-primary-light)';
+            } else {
+            document.body.classList.add('dark');
+            document.querySelector('gradio-app').style.backgroundColor = 'var(--color-background-primary-dark)';
+            }
+        }
+        """,
+    )
+
+with gr.Blocks(
+    css="""#chat_container {height: 820px; width: 1000px; margin-left: auto; margin-right: auto;}
+            #chatbot {height: 600px; overflow: auto;}
+            #create_container {height: 750px; margin-left: 0px; margin-right: 0px;}
+            #tokenizer_renderer span {white-space: pre-wrap}
+            """,
+    theme="gradio/monochrome",
+) as demo:
+    # with gr.Row():
+    #     navigation_bar()
+    # gr.Image(
+    #         "images/banner.jpg", elem_id="banner-image", show_label=False
+    #     )
+    # gr.Markdown(HEADER_MD, elem_classes="markdown-text")
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        with gr.TabItem("🏅 Leaderboard", elem_id="benchmark-tab-table", id=0):
+            benchmark.benchmark_table()
+        # with gr.TabItem("💬 Chat", elem_id="chat-tab-interface", id=1): 
+        #     with gr.Row():
+        #         chat_introduction()
+        #     with gr.Row():
+        #         chat_tab()
+    with gr.Row():
+        with gr.Accordion("📙 Citation", open=False, elem_classes="accordion-label"):
+            gr.Textbox(
+                value=CITATION_TEXT, 
+                lines=7,
+                label="Copy the BibTeX snippet to cite this source",
+                elem_id="citation-button",
+                show_copy_button=True)
+
+# def start_demo():
+#     demo = main()
+#     if DEPLOYED:
+#         demo.queue(api_open=False).launch(show_api=False)
+#     else:
+#         demo.queue()
+#         demo.launch(share=False, server_name="0.0.0.0")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--result_file", help="Path to results table", default="data_dir\\bartscore_100_results.jsonl")
+    #benchmark.original_df = pd.read_json(args.result_file, lines=True)
+    # get_sotopia_profiles()
+    # prepare_model(DEFAULT_MODEL_SELECTION)
+    demo.launch()

convert_csv_to_jsonl.py

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+import os
+import pandas as pd
+import json
+
+csv_path = 'D:\Work\sotopia-space\hemm_results_all.xlsx'
+data_dir = ''
+df = pd.read_excel(csv_path, sheet_name='bartscore_100_samples')
+# print(df.columns)
+models = df['Models']
+df = df.iloc[:,2:]
+columns = df.columns
+# print(df.columns)
+# print(len(df))
+data_list = []
+for model_index, model in enumerate(models):
+    model_dict = {"Model Name":model}
+    for dataset_index, dataset in enumerate(columns):
+        model_dict[dataset] = df.iloc[model_index, dataset_index]
+    data_list.append(model_dict)
+
+with open("bartscore_100_results.jsonl", 'w') as f:
+    for item in data_list:
+        f.write(json.dumps(item) + "\n")

data_dir/bartscore_100_results.jsonl

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+{"Model Name": "minigpt4", "vqa": -6.04999752759933, "nocaps": -2.65412774443626, "decimer": -5.11561377763748, "memotion": -4.94760845184326, "scienceqa": -5.43017168283462, "slake": -6.0269759941101, "visualgen": -4.40035352766513, "pathvqa": -5.58624832868576, "ucmerced": -6.20994124412536, "enrico": -7.31821565628051, "mmimdb": -4.08943289698976, "vqarad": -5.54124823676215, "flickr30k": -2.8887563663721, "faceemotion": -6.74521959304809, "newyorkercartoon": -5.38009900569915, "okvqa": -5.21686240375042, "magicbrush": 0.0, "memecaps": -4.0054063129425, "hatefulmemes": -6.41417801856994, "inat": -6.35647469043731, "nlvr": -4.51981778860092, "nlvr2": -6.06691724807024, "vcr": -4.81910507678985, "winoground": -5.77940632939338, "resisc45": -6.38264159917831, "GQA": -5.56431758880615, "plip": -6.49534327507019, "irfl": -5.78215121746063, "screen2words": -6.31326360702514}
+{"Model Name": "fuyu", "vqa": -4.73486727952957, "nocaps": -2.32671338796615, "decimer": -5.2730155801773, "memotion": -4.33607249736785, "scienceqa": -4.32769961193203, "slake": -4.62900659561157, "visualgen": -3.5822375011444, "pathvqa": -5.25311197996139, "ucmerced": -6.15809995651245, "enrico": -6.48817044973373, "mmimdb": -5.25345686951068, "vqarad": -5.49303081830342, "flickr30k": -2.59373948395252, "faceemotion": -6.39009295225143, "newyorkercartoon": -5.72272732019424, "okvqa": -4.04719371974468, "magicbrush": 0.0, "memecaps": -4.26145068645477, "hatefulmemes": -5.14756870388984, "inat": -6.08853887081146, "nlvr": -3.46463850975036, "nlvr2": -3.81220122426748, "vcr": -3.3061693817377, "winoground": -7.52705668330192, "resisc45": -5.92823511242866, "GQA": -4.21522821426391, "plip": -6.04749953985214, "irfl": -5.58978553056716, "screen2words": -5.97407633781433}
+{"Model Name": "kosmos2", "vqa": -6.00090564727783, "nocaps": -2.18709655851125, "decimer": -5.12371077299118, "memotion": -5.59075596094131, "scienceqa": -5.31471309185028, "slake": -5.44800034999847, "visualgen": -5.27276257276535, "pathvqa": -5.78720266342163, "ucmerced": -6.16654364585876, "enrico": -7.63494473934173, "mmimdb": -5.07747607760959, "vqarad": -7.20146285163031, "flickr30k": -2.53884373426437, "faceemotion": -6.60952225208282, "newyorkercartoon": -6.05882290840148, "okvqa": -5.33590672969818, "magicbrush": 0.0, "memecaps": -3.5750606060028, "hatefulmemes": -4.80262948989868, "inat": -6.13972020626068, "nlvr": -3.94609386444091, "nlvr2": -6.2186327278614, "vcr": -4.67466379880905, "winoground": -3.94123034000396, "resisc45": -7.0790620470047, "GQA": -5.59488793849945, "plip": -5.65299202919006, "irfl": -4.63381761074066, "screen2words": -5.94307591676712}
+{"Model Name": "instruct_blip", "vqa": -4.77069588661193, "nocaps": -2.14318353235721, "decimer": -5.17403265714645, "memotion": -4.61455537080764, "scienceqa": -2.93838927194476, "slake": -4.2282608628273, "visualgen": -3.89980739831924, "pathvqa": -5.16406374692916, "ucmerced": -4.35503688812255, "enrico": -7.62606196403503, "mmimdb": -4.72763555940955, "vqarad": -5.34427996476491, "flickr30k": -2.49245727658271, "faceemotion": -3.4075683927536, "newyorkercartoon": -4.67849154472351, "okvqa": -3.54418572247028, "magicbrush": 0.0, "memecaps": -4.47347595930099, "hatefulmemes": -4.25790207743644, "inat": -6.09216739892959, "nlvr": -3.54432603836059, "nlvr2": -3.79536471888422, "vcr": -3.28695653975009, "winoground": -4.03675424933433, "resisc45": -5.51440529942512, "GQA": -4.22285702466964, "plip": -4.59053925514221, "irfl": -4.54026131272315, "screen2words": -6.02177966833114}
+{"Model Name": "blip2", "vqa": -5.35926390528678, "nocaps": -2.32535250216722, "decimer": -5.25810585021972, "memotion": -4.66036592721939, "scienceqa": -2.4944266308844, "slake": -4.63699433565139, "visualgen": -4.13378090500831, "pathvqa": -5.63342162132263, "ucmerced": -3.8299672204256, "enrico": -6.69461050629615, "mmimdb": -3.80532048928617, "vqarad": -5.03765134016672, "flickr30k": -2.57776173233985, "faceemotion": -3.50588282585144, "newyorkercartoon": -4.78030658483505, "okvqa": -4.17335272789001, "magicbrush": 0.0, "memecaps": -4.39764193773269, "hatefulmemes": -4.62208485126495, "inat": -5.88216744422912, "nlvr": -3.22551344513893, "nlvr2": -3.43102744035422, "vcr": -3.20449171781539, "winoground": -3.88114604830741, "resisc45": -4.33277277827262, "GQA": -4.76980807304382, "plip": -6.98160505294799, "irfl": -4.29795031905174, "screen2words": -5.92229708671569}
+{"Model Name": "openflamingo", "vqa": -6.1161942744255, "nocaps": -3.22710797071456, "decimer": -5.09846289634704, "memotion": -5.97115942478179, "scienceqa": -6.31871804475784, "slake": -5.84101435184478, "visualgen": -4.10811762571334, "pathvqa": -6.16680700540542, "ucmerced": -6.5275741815567, "enrico": -7.72299034595489, "mmimdb": -5.31553883504385, "vqarad": -5.9087261252933, "flickr30k": -3.68372164964675, "faceemotion": -7.19220809936523, "newyorkercartoon": -7.60312170505523, "okvqa": -6.04300261497497, "magicbrush": 0.0, "memecaps": -4.67648088932037, "hatefulmemes": -5.79277225971221, "inat": -5.90263050079345, "nlvr": -5.47891213417053, "nlvr2": -6.98076025396585, "vcr": -6.30748137950897, "winoground": -7.18134065628051, "resisc45": -6.80881009101867, "GQA": -5.63211553812027, "plip": -6.1712994146347, "irfl": -7.0914549422264, "screen2words": -7.06239040374755}
+{"Model Name": "llama_adapter", "vqa": -5.96558716535568, "nocaps": -2.49247494459152, "decimer": -4.68053029060363, "memotion": -5.45314621210098, "scienceqa": -5.0200485676527, "slake": -5.24185500621795, "visualgen": -4.42215523004531, "pathvqa": -5.56947420120239, "ucmerced": -6.53108041286468, "enrico": -6.97309422969818, "mmimdb": -4.47780330012543, "vqarad": -6.00346731609768, "flickr30k": -3.40018630504608, "faceemotion": -6.29382851600647, "newyorkercartoon": -6.37209278106689, "okvqa": -5.25631190657615, "magicbrush": 0.0, "memecaps": -4.00828188180923, "hatefulmemes": -6.38690366268157, "inat": -5.88109484195709, "nlvr": -4.87403306961059, "nlvr2": -6.94134422391653, "vcr": -4.23472047448158, "winoground": -5.61739140033721, "resisc45": -6.60460837841033, "GQA": -5.63892419815063, "plip": -5.96916566848754, "irfl": -5.12358436346054, "screen2words": -6.23817425727844}
+{"Model Name": "mplugowl", "vqa": -5.98267959356308, "nocaps": -2.33945092737674, "decimer": -4.88617345809936, "memotion": -5.05114650964737, "scienceqa": -4.97901670455932, "slake": -5.53747601032257, "visualgen": -5.58307039022445, "pathvqa": -5.53253057718276, "ucmerced": -6.02884557723999, "enrico": -6.79997408866882, "mmimdb": -4.58671936121853, "vqarad": -7.10166616439819, "flickr30k": -2.67252381086349, "faceemotion": -5.91813237190246, "newyorkercartoon": -6.17466721057891, "okvqa": -5.27141753911972, "magicbrush": 0.0, "memecaps": -3.77229596376419, "hatefulmemes": -5.61374977588653, "inat": -5.83331126451492, "nlvr": -5.8032091808319, "nlvr2": -5.91577186062932, "vcr": -4.15608766913414, "winoground": -5.48709903717041, "resisc45": -6.63243839740753, "GQA": -5.67177940845489, "plip": -5.89347852706909, "irfl": -4.9557000541687, "screen2words": -6.01361167907714}
+{"Model Name": "emu", "vqa": -4.82976653456687, "nocaps": -2.58528818786144, "decimer": -5.33891901969909, "memotion": -6.13612433433532, "scienceqa": -5.31471309185028, "slake": -5.85805670976638, "visualgen": -5.24631004095077, "pathvqa": -5.41462924480438, "ucmerced": -6.35667475461959, "enrico": -7.55213621377944, "mmimdb": -5.36864378837623, "vqarad": -6.64751581615871, "flickr30k": -3.57265928745269, "faceemotion": -7.66139155387878, "newyorkercartoon": -6.61696485042572, "okvqa": -5.10015687465667, "magicbrush": 0.0, "memecaps": -4.69071522712707, "hatefulmemes": -7.09997977137565, "inat": -5.91944311857223, "nlvr": -6.89201524734497, "nlvr2": -6.3121773302555, "vcr": -6.3945353770256, "winoground": -7.82299115180969, "resisc45": -5.85876505136489, "GQA": -4.75100205540657, "plip": -7.0092953300476, "irfl": -4.80120725154876, "screen2words": -7.36161555290222}
+{"Model Name": "gptv", "vqa": -5.55994161844253, "nocaps": -2.48879728913307, "decimer": -4.48280839681625, "memotion": -5.60457567691803, "scienceqa": -2.40498170137405, "slake": -4.8690076804161, "visualgen": -3.76253581285476, "pathvqa": -5.55322549939155, "ucmerced": -4.64215887844562, "enrico": -6.38901618003845, "mmimdb": -3.3945183832236, "vqarad": -5.37530589103698, "flickr30k": -2.93780707120895, "faceemotion": -4.18342504024505, "newyorkercartoon": -6.10919400691986, "okvqa": -4.37436116099357, "magicbrush": 0.0, "memecaps": -3.70642701387405, "hatefulmemes": -6.37660904049873, "inat": -5.97897940993309, "nlvr": -3.29634021043777, "nlvr2": -2.45676662959158, "vcr": -4.26847109436988, "winoground": -3.83495727539062, "resisc45": -5.20133357286453, "GQA": -4.98619713544845, "plip": -5.05113951683044, "irfl": -4.24152393817901, "screen2words": -5.54262929201126}
+{"Model Name": "gemini", "vqa": -4.83165967345237, "nocaps": -2.43551394641399, "decimer": -4.25038826942443, "memotion": -4.72992189645767, "scienceqa": -4.04351055532693, "slake": -3.97932574033737, "visualgen": -3.73516181886196, "pathvqa": -4.9108063840866, "ucmerced": -4.45181241750717, "enrico": -6.33585796236991, "mmimdb": -3.256920427385, "vqarad": -5.32404233084784, "flickr30k": -2.56568041443824, "faceemotion": -4.02943905711174, "newyorkercartoon": -4.88852659702301, "okvqa": -4.01371744036674, "magicbrush": 0.0, "memecaps": -3.28478899598121, "hatefulmemes": -3.63258032321929, "inat": -5.02380921483039, "nlvr": -3.48708863735199, "nlvr2": -3.73220004513859, "vcr": -3.37185937017202, "winoground": -4.09753547430038, "resisc45": -5.22460073709487, "GQA": -4.66138199329376, "plip": -5.57941418409347, "irfl": -4.16364158391952, "screen2words": -5.71829539537429}

data_dir/models_vs_gpt35.jsonl

@@ -0,0 +1,6 @@
+{"model_name": "GPT-4", "SOC [-10, 0]": -0.07, "SEC [-10, 0]": -0.14, "FIN [-5, 5]": 0.81, "REL [-5, 5]": 1.94, "KNO [0, 10]": 3.73, "GOAL [0, 10]": 7.62, "BEL [0, 10]": 9.28}
+{"model_name": "GPT-3.5", "SOC [-10, 0]": -0.08, "SEC [-10, 0]": -0.08, "FIN [-5, 5]": 0.46, "REL [-5, 5]": 1.23, "KNO [0, 10]": 3.4, "GOAL [0, 10]": 6.45, "BEL [0, 10]": 9.15}
+{"model_name": "Llama-2", "SOC [-10, 0]": -0.11, "SEC [-10, 0]": -0.14, "FIN [-5, 5]": 0.4, "REL [-5, 5]": 0.91, "KNO [0, 10]": 3.11, "GOAL [0, 10]": 5.38, "BEL [0, 10]": 8.1}
+{"model_name": "MPT", "SOC [-10, 0]": -0.09, "SEC [-10, 0]": -0.07, "FIN [-5, 5]": 0.28, "REL [-5, 5]": 0.58, "KNO [0, 10]": 2.11, "GOAL [0, 10]": 4.1, "BEL [0, 10]": 6.17}
+
+// {"model_name":}

example.json

@@ -0,0 +1,5 @@
+{
+    "model": "gpt-4",
+    "prompt": "Prompt after formatting:\nImagine you are Ethan Smith, your task is to act/speak as Ethan Smith would, keeping in mind Ethan Smith's social goal.\nYou can find Ethan Smith's background and goal in the 'Here is the context of the interaction' field.\nNote that Ethan Smith's secret and goal is only visible to you.\nYou should try your best to achieve Ethan Smith's goal in a way that align with their character traits.\nAdditionally, maintaining the conversation's naturalness and realism is essential (e.g., do not repeat what other people has already said before).\n\nHere is the context of this interaction:\nScenario: Two friends are at a community gathering where one of them was giving a speech but was interrupted\nParticipants: Hendrick Heinz and Ethan Smith\nHendrick Heinz's background: Hendrick Heinz is a 54-year-old male chef. He/him pronouns. Hendrick Heinz hosts monthly wine tasting nights with his peers. Personality and values description: Hendrick Heinz, though somewhat impulsive, values hedonism and conformity. He combines practicality with decisiveness in decision-making. \nEthan Smith's background: Ethan Smith is a 29-year-old male software developer. He/him pronouns. Ethan enjoys reading about new technological advancements and watching documentaries. Personality and values description: Ethan Smith is a reserved, independent thinker. Holding universalism and fairness near, he ponders deeply before making decisions. Ethan's secrets: Secretly donates to charities for underprivileged children\nHendrick Heinz's goal: Unknown\nEthan Smith's goal: Apologize for interrupting and allow them to continue speaking (Extra information: You accidentally interrupted someone's speech and everyone seems upset about it. )\nConversation Starts:\n\nTurn #0: Hendrick Heinz said: \"Well, where was I? Ah yes, let me continue. As I was saying, sharing gourmet knowledge is not just about showing off, but rather enhancing the experience..\"\n\nTurn #1: Ethan Smith said: \"Hendrick, I sincerely apologize for my earlier interruption. Please, go on about enhancing the experience. I'm quite interested in your perspective on gourmet knowledge sharing.\"\n\nTurn #2: Hendrick Heinz said: \"Not a problem, Ethan. Anyone can get excited when talking about something they love, right? Now, back to our topic. I believe gourmet knowledge makes every meal an exploration. When you know the story, it adds another layer to the taste. Food becomes more than a means of survival; it becomes an experience. And in the end, isn't life about enjoying every experience to its fullest?\"\n\nTurn #3: Ethan Smith said: \"You're absolutely right, Hendrick. The joy of food extends far beyond sustenance, it's the stories and experiences that truly enrich it. Thanks for sharing this insightful perspective. Please, continue.\"\n\nTurn #4: \n\nTurn #5: Ethan Smith said: \"Absolutely. I've long admired your ability to turn food into an experience, Hendrick. You have turned these gatherings into a journey, and it's something I sure look forward to every month. Please, continue on with your speech.\"\n\nTurn #6: Hendrick Heinz said: \"Thank you, Ethan. Your support truly means a lot to me. Moving forward, let me share how the skill of artfully pairing food and wine can totally transform a meal. The blissful marriage of the right wine and food intensifies the overall dining experience by complementing and highlighting the unique flavors. It\u2019s a skill, and when you master it, every meal becomes a feast for your senses.\"\n\nTurn #7: Ethan Smith said: \"That's fascinating, Hendrick. The art of pairing is an aspect I haven't considered much before. It's enlightening to hear about the transformative potential of the right food with the perfect wine. I appreciate your shared insights today.\"\n\nTurn #8: Hendrick Heinz said: \"Absolutely, Ethan. The right pairing is like finding the perfect harmony in a symphony. Each component, food and wine, with their unique attributes, contribute to the crescendo of flavors when properly united. It\u2019s about symmetry and balance. Think of a well-aged cheese with a rich, buttery Chardonnay. The creamy opulence of the cheese is beautifully echoed in the velvety textures of the wine. It's harmony on your palate, the ultimate form of indulgence.\"\n\nTurn #9: Ethan Smith said: \"That's an enlightening way to put it, Hendrick. The art of food and wine pairing truly seems like a symphony of flavors. It's clear now how the right pairing can uplift a dining experience. Thank you for sharing these expert insights. I now look forward to exploring this aspect of gourmet dining.\"\n\nTurn #10: Hendrick Heinz said: \"I'm pleased to hear that you're intrigued, Ethan. To further elaborate, think about contrasting flavors as well. A sweet wine might not be the first choice for an appetizer like foie gras due to its richness, but the sweetness can cut through the fat and create a balance, sending your taste buds on a journey. That's all from me tonight. Let's continue our own exploration and enjoy the rest of the evening with our fine wine and food. Cheers!\"\n.\nYou are at Turn #11.",
+    "result": "{'action_type': 'speak', 'argument': \"Cheers to that, Hendrick! You've truly opened a new realm of dining for me tonight. I now understand the beauty of gourmet knowledge, how the blending of flavors can create a symphony in our palate. Can't wait to embark on my own food and wine pairing journey. Thank you.\"}"
+}

langchain_callback_handler.py

@@ -0,0 +1,60 @@
+import logging
+from typing import Any
+
+from langchain.callbacks import StdOutCallbackHandler
+
+logging.addLevelName(15, "LangChain")
+
+
+class LoggingCallbackHandler(StdOutCallbackHandler):
+    """Callback Handler that prints to std out."""
+
+    always_verbose = True
+
+    def __init__(self, name: str) -> None:
+        """Initialize callback handler."""
+        super().__init__()
+        self.logger = logging.getLogger(name)
+        self.prompt = ""
+
+    def on_chain_start(self, *args: Any, **kwargs: Any) -> None:
+        pass
+
+    def on_chain_end(self, *args: Any, **kwargs: Any) -> None:
+        pass
+
+    def on_agent_action(self, *args: Any, **kwargs: Any) -> Any:
+        pass
+
+    def on_tool_end(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        pass
+
+    def on_tool_error(
+        self, error: BaseException | KeyboardInterrupt, **kwargs: Any
+    ) -> None:
+        """Do nothing."""
+        pass
+
+    def on_text(
+        self,
+        text: str,
+        color: str | None = None,
+        end: str = "",
+        **kwargs: Any,
+    ) -> None:
+        """Run when agent ends."""
+        # leave only prompt for environment
+        text = text.replace("\x1b[32;1m\x1b[1;3mHuman: ", "")
+        logging.log(15, f"LLM Call: {text}")
+        self.prompt = text
+
+    def retrive_prompt(self) -> str:
+        return self.prompt
+
+    def on_agent_finish(self, *args: Any, **kwargs: Any) -> None:
+        """Run on agent end."""
+        pass

message_classes.py

@@ -0,0 +1,343 @@
+import re
+from typing import Literal, cast
+
+from pydantic import BaseModel, Field
+
+from utils import format_docstring
+
+ActionType = Literal["none", "speak", "non-verbal communication", "action", "leave"]
+
+
+class Message(BaseModel):
+    """
+    An interface for messages.
+    There is only one required method: to_natural_language
+    """
+
+    def to_natural_language(self) -> str:
+        raise NotImplementedError
+
+
+class SimpleMessage(Message):
+    """
+    A simple message with a single string field.
+    """
+
+    message: str = Field(description="the message")
+
+    def to_natural_language(self) -> str:
+        return self.message
+
+
+class Observation(Message):
+    last_turn: str = Field(description="the last turn of the conversation")
+    turn_number: int = Field(description="the turn number of the conversation")
+    available_actions: list[ActionType] = Field(description="the available actions")
+
+    def to_natural_language(self) -> str:
+        if self.turn_number == 0:
+            return f"\n{self.last_turn}\nConversation Starts:\n"
+        else:
+            return f"Turn #{self.turn_number-1}: {self.last_turn}\n"
+
+
+class ScriptBackground(Message):
+    scenario: str = Field(description="scenario of the episode")
+    p1_name: str = Field(description="name of participant 1")
+    p2_name: str = Field(description="name of participant 2")
+    p1_background: str = Field(description="background of participant 1")
+    p2_background: str = Field(description="background of participant 2")
+    p1_goal: str = Field(description="goal of participant 1")
+    p2_goal: str = Field(description="goal of participant 2")
+
+    def to_natural_language(self) -> str:
+        if self.p1_background or self.p2_background:
+            p1_background = self.p1_background if self.p1_background else "Unknown"
+            p2_background = self.p2_background if self.p2_background else "Unknown"
+            # Not using AND, since in stranger relation the background is not visible
+            return format_docstring(
+                f"""Here is the context of this interaction:
+            Scenario: {self.scenario}
+            Participants: {self.p1_name} and {self.p2_name}
+            {self.p1_name}'s background: {p1_background}
+            {self.p2_name}'s background: {p2_background}
+            {self.p1_name}'s goal: {self.p1_goal}
+            {self.p2_name}'s goal: {self.p2_goal}
+            """
+            )
+        else:
+            return format_docstring(
+                f"""Here is the context of this interaction:
+            Scenario: {self.scenario}
+            Participants: {self.p1_name} and {self.p2_name}
+            {self.p1_name}'s goal: {self.p1_goal}
+            {self.p2_name}'s goal: {self.p2_goal}
+            """
+            )
+
+
+class ScriptEnvironmentResponse(Message):
+    terminated: bool = Field(
+        description="whether the conversation is terminated",
+        default_factory=lambda: False,
+    )
+    p1_rate: float | tuple[float, dict[str, float]] | None = Field(
+        description="rating of participant 1, on the scale of 1 to 10"
+    )
+    p2_rate: float | tuple[float, dict[str, float]] | None = Field(
+        description="rating of participant 2, on the scale of 1 to 10"
+    )
+    comments: str | None = Field(
+        description="All of the comments supporting the termination and rating"
+    )
+
+    def to_natural_language(self) -> str:
+        reason_to_stop = format_docstring(
+            f"""Environment response:
+        {"The conversation is terminated." if self.terminated else ""}
+        {"Rating of participant 1" + str(self.p1_rate) if self.p1_rate is not None else ""}
+        {"Rating of participant 2" + str(self.p2_rate) if self.p2_rate is not None else ""}
+        {self.comments if self.comments is not None else ""}
+        """
+        )
+        clean_text = ""
+        for line in reason_to_stop.split("\n"):
+            if line.strip():
+                clean_text += line + "\n"
+        return clean_text
+
+
+class AgentAction(Message):
+    action_type: ActionType = Field(
+        description="whether to speak at this turn or choose to not do anything"
+    )
+    argument: str = Field(
+        description="the utterance if choose to speak, the expression or gesture if choose non-verbal communication, or the physical action if choose action"
+    )
+
+    def to_natural_language(self) -> str:
+        match self.action_type:
+            case "none":
+                return "did nothing"
+            case "speak":
+                return f"{self.argument}"
+            case "non-verbal communication":
+                return f"[{self.action_type}] {self.argument}"
+            case "action":
+                return f"[{self.action_type}] {self.argument}"
+            case "leave":
+                return "left the conversation"
+
+
+ScriptInteractionReturnType = tuple[
+    list[list[tuple[str, str, Message]]], list[tuple[str, Message]]
+]
+
+
+class ScriptInteraction(Message):
+    interactions: str = Field(
+        description="""The interaction between the two participants in maximum 20 turns. Each turn is separated by a newline, and should only describe one agent. Following the structure:
+        Turn #x
+        [participant's name] [action] {argument for some actions}
+
+        You can use different types of actions, but only use one in each turn. You should move other information into argument part. Below shows a python code snippet of the format for each action type:
+        match self.action_type:
+            case "none":
+                return "did nothing"
+            case "speak":
+                return f'said: "{self.argument}"'
+            case "non-verbal communication":
+                return f"[{self.action_type}] {self.argument}"
+            case "action":
+                return f"[{self.action_type}] {self.argument}"
+            case "leave":
+                return "left the conversation"
+
+        For example, the following is acceptable:
+        Turn #x
+        Oliver Thompson said: "Hey Esmeralda, what's wrong? You seem upset."
+        Turn #x
+        Esmeralda Solis [action] moved closer
+        Turn #x
+        Oliver Thompson [non-verbal communication] smiled
+        Turn #x
+        Esmeralda Solis did nothing
+        Turn #x
+        Oliver Thompson left the conversation
+        Turn #x
+        Esmeralda Solis [action] leaned in and lowered her voice: "Sorry"
+
+        And the following is not acceptable:
+        Turn #1
+        Oliver Thompson [speak] said: "Hey Esmeralda, what's wrong? You seem upset."
+        Turn #1
+        Esmeralda Solis non-verbal communication moved closer
+        """
+    )
+
+    def to_natural_language(self) -> str:
+        return self.interactions
+
+    def parse(
+        self, agent_names: list[str], background: str
+    ) -> tuple[list[list[tuple[str, str, Message]]], list[tuple[str, Message]]]:
+        interaction = self.interactions
+        # print("Interaction: ", interaction)
+        lines = self.split_by_turn(interaction)
+
+        agent_results = []
+        results: list[list[tuple[str, str, Message]]] = [
+            [
+                (
+                    "Environment",
+                    name,
+                    Observation(
+                        last_turn=background,
+                        turn_number=0,
+                        available_actions=["none"],
+                    ),
+                )
+                for name in agent_names
+            ]
+        ]
+
+        for line_idx, line in enumerate(lines):
+            try:
+                res = self.parse_single_dialogue(line)
+                action: AgentAction = cast(AgentAction, res["action"])
+                argument: str = cast(str, res["argument"])
+                cast(int, res["turn"])
+                name: str = cast(str, res["name"])
+
+                parsed_action = AgentAction(action_type=action, argument=argument)
+                if name not in agent_names:
+                    print(
+                        f"The name of the agent, {name}, is not in the list of agent names, {agent_names}"
+                    )
+                    name = agent_names[
+                        line_idx % 2
+                    ]  # TODO Not sure what name to be set here
+            except Exception as e:
+                print(
+                    f"Error when parsing the dialogue: {line}",
+                    f"The error is: {e}",
+                )
+                raise e
+                parsed_action = AgentAction(action_type="none", argument="")
+                name = agent_names[line_idx % 2]  # TODO same question as above
+            inactive_agent_name = (
+                agent_names[0] if name == agent_names[1] else agent_names[1]
+            )
+            results.append(
+                [
+                    (
+                        "Environment",
+                        name,
+                        Observation(
+                            last_turn="environment is the agent",
+                            turn_number=line_idx + 1,
+                            available_actions=["none"],
+                        ),
+                    )
+                    for name in agent_names
+                ]
+                + [
+                    (name, "Environment", parsed_action),
+                    (
+                        inactive_agent_name,
+                        "Environment",
+                        AgentAction(action_type="none", argument="did nothing"),
+                    ),
+                ]
+            )
+
+            agent_results.append((name, parsed_action))
+        # print("Parsed agent results: ", agent_results)
+        return (results, agent_results)  # type: ignore
+
+    def parse_single_dialogue(
+        self, dialogue: str
+    ) -> dict[str, str | int | AgentAction | None]:
+        """Parse a single dialogue string and return a dictionary with turn, name, action, and argument."""
+
+        # Match the turn number and name. Assume all agent name starts with a capital letter and is followed by lowercase letters
+        match_turn_name = re.match(
+            r"Turn #?(\d+):?\s*\n((?:[A-Z]['a-z]* ?)+)", dialogue
+        )
+
+        if not match_turn_name:
+            raise ValueError(
+                f"The dialogue does not match the expected format: {dialogue}"
+            )
+            return None  # TODO Which should we use, return None or raise error?
+
+        turn, name = match_turn_name.groups()
+        action_content = dialogue[
+            len(match_turn_name.group(0)) :
+        ].strip()  # Extract the action content
+
+        # Check for different action types
+        if "did nothing" in action_content:
+            action, argument = "none", ""
+        elif match := re.match(r'said: "(.*?)"', action_content):
+            action, argument = "speak", match.group(1)
+            action, argument = action.strip(), argument.strip()
+        elif match := re.match(r'\[speak\] said: "(.*?)"', action_content):
+            action, argument = "speak", match.group(1)
+            action, argument = action.strip(), argument.strip()
+        elif match := re.match(
+            r"\[(non-verbal communication|action)\] (.*)", action_content
+        ):
+            action, argument = match.groups()
+        elif "left the conversation" in action_content:
+            # TODO Make it more elegant to handle the situation of `left the conversation.`
+            action, argument = "leave", ""
+        else:
+            action, argument = None, None
+
+        parsed_item = {
+            "turn": int(turn),
+            "name": name.strip(),
+            "action": action,
+            "argument": argument,
+        }
+        return parsed_item
+
+    def split_by_turn(self, input_string: str) -> list[str]:
+        """Split the input dialogue string by turn and return a list of dialogues."""
+        # Split using 'Turn #' as delimiter, but keep the delimiter in the results
+        dialogues = re.split(r"(?=Turn #?\d+)", input_string)
+        # Remove any empty strings and strip whitespace
+        dialogues = [dialogue.strip() for dialogue in dialogues if dialogue.strip()]
+        dialogues = [dialogue for dialogue in dialogues if dialogue.startswith("Turn")]
+        # Change from Turn #x to Turn (#)x (# is optional)
+        dialogues[-1] = "\n".join(
+            dialogues[-1].split("\n")[:2]
+        )  # Discard further input in the last turn
+
+        for dialogue in dialogues:
+            # TODO this is current workaround for the issue of multiple agents in one turn
+            if len(dialogue.split("\n")) >= 3:
+                raise ValueError("Only one agent can act per turn.")
+        return dialogues
+
+    @staticmethod
+    def default_value_for_return_type() -> ScriptInteractionReturnType:
+        results_1: list[list[tuple[str, str, Message]]] = [
+            [
+                (
+                    "Environment",
+                    name,
+                    Observation(
+                        last_turn="Environment is the agent",
+                        turn_number=0,
+                        available_actions=["none"],
+                    ),
+                )
+                for name in ["none", "none"]
+            ]
+        ]
+        results_2: list[tuple[str, Message]] = [
+            ("", AgentAction(action_type="none", argument=""))
+        ]
+        return (results_1, results_2)

profiles/agent_profiles.jsonl

@@ -0,0 +1,40 @@
+{"agent_id": "01H5TNE5PRCAF1CK5ERS5MVZ22", "first_name": "Amara", "last_name": "Hartley", "age": 29, "occupation": "Wildlife Biologist", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Amara Hartley spends her free time helping endangered animals and is a nature enthusiast. Her passion for animals and the environment is notable.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - Low; Agreeableness - High; Neuroticism - Low", "moral_values": ["Care", " Fairness"], "schwartz_personal_values": ["Universalism", " Benevolence", " Self-Direction"], "personality_and_values": "Amara Hartley is open-minded, ambiverted, and can be emotionally volatile. A believer in universalism and benevolence, Amara is analytical and intuitive in her decision-making.", "decision_making_style": "Analytica, intuitive", "secret": "Secretly involved in animal-rights extremism", "mbti": "INFJ"}
+{"agent_id": "01H5TNE5PR54W6SFXKNQWKGPAS", "first_name": "Baxter", "last_name": "Sterling", "age": 42, "occupation": "Antique Restorer", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Baxter Sterling's back yard is full of rare and unusual plants.", "big_five": "Openness to Experience - Low; Conscientiousness - Low; Extraversion - Low; Agreeableness - Low; Neuroticism - High", "moral_values": ["Loyalty", " Liberty"], "schwartz_personal_values": ["Tradition", " Achievement", " Security"], "personality_and_values": "Baxter Sterling values tradition and security. He is introverted, conscientious but slightly anxious. His decision-making style is both logical and spontaneous.", "decision_making_style": "Logical, spontaneous", "secret": "Secretly in possession of a priceless artifact", "mbti": "ISTP"}
+{"agent_id": "01H5TNE5PWZ5PNDTGKDYRY36PQ", "first_name": "Giselle", "last_name": "Rousseau", "age": 21, "occupation": "Art Student", "gender": "Nonbinary", "gender_pronoun": "They/them", "public_info": "Giselle Rousseau enjoys biking and photography.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - High; Agreeableness - High; Neuroticism - Low", "moral_values": ["Care", " Fairness"], "schwartz_personal_values": ["Stimulation", " Self-Direction", " Achievement"], "personality_and_values": "Giselle Rousseau, open-minded and outgoing yet sensitive, advocates care and fairness. Her decision-making is intuitive and inclusive.", "decision_making_style": "Intuitive, collaborative", "secret": "Sells forged paintings to wealthy clients", "mbti": "ENFJ"}
+{"agent_id": "01H5TNE5P7RVY0TYX8VTCXABR6", "first_name": "Sophia", "last_name": "James", "age": 27, "occupation": "Personal Trainer", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Sophia enjoys spending time outdoors, hiking, and rock climbing.", "big_five": "Openness to Experience - Low; Conscientiousness - Low; Extraversion - High; Agreeableness - High; Neuroticism - High", "moral_values": ["Fairness/Cheating"], "schwartz_personal_values": ["Hedonism"], "personality_and_values": "Sophia James is exuberant, spontaneous, and pleasure-seeking, often making impulsive decisions. ", "decision_making_style": "Spontaneous", "secret": "Secretly in love with her best friend's husband", "mbti": "ESFP"}
+{"agent_id": "01H5TNE5PFT9HH0WRT6W1NY5GZ", "first_name": "Leo", "last_name": "Williams", "age": 37, "occupation": "Dentist", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Leo Williams often spends his time playing with his young daughter whom he cherishes dearly.", "big_five": "Openness to Experience - Low; Conscientiousness - High; Extraversion - High; Agreeableness - High; Neuroticism - Low", "moral_values": ["Care/Harm"], "schwartz_personal_values": ["Security"], "personality_and_values": "Leo Williams, despite being quiet, values security and care for others. He's a rational decision-maker focussing on best outcomes.", "decision_making_style": "Rational", "secret": "He has a secret child from a previous relationship.", "mbti": "ESFJ"}
+{"agent_id": "01H5TNE5P83CZ1TDBVN74NGEEJ", "first_name": "William", "last_name": "Brown", "age": 35, "occupation": "Chef", "gender": "Man", "gender_pronoun": "He/him", "public_info": "William Brown loves exploring the food scene in his city and trying out new recipes at home.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - High; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Care/Harm"], "schwartz_personal_values": ["Achievement"], "personality_and_values": "William Brown is an ambitious and extraverted individual. Methodical and organized, he values achievement and displays strategic decision-making.", "decision_making_style": "Strategic", "secret": "Sabotaged a competitor's dish to win a cooking contest", "mbti": "ENTJ"}
+{"agent_id": "01H5TNE5PC2DP6RKG0KNQ4D5GE", "first_name": "Samuel", "last_name": "Anderson", "age": 29, "occupation": "Software Developer", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Samuel Anderson can cook very well.", "big_five": "Openness to Experience - Low; Conscientiousness - High; Extraversion - Low; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Sanctity/Purity"], "schwartz_personal_values": ["Hedonism"], "personality_and_values": "Samuel Anderson, though somewhat impulsive and free-spirited, values enjoyment. His decision-making is often spontaneous, staying within familiar boundaries.", "decision_making_style": "Spontaneous", "secret": "He was once a competitive figure skater.", "mbti": "ISTJ"}
+{"agent_id": "01H5TNE5PDV7WZ0C5KTGGXX1NR", "first_name": "Isabelle", "last_name": "Martinez", "age": 22, "occupation": "Student", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Isabelle Martinez works part-time in a coffee shop.", "big_five": "Openness to Experience - High; Conscientiousness - Low; Extraversion - High; Agreeableness - High; Neuroticism - High", "moral_values": ["Liberty"], "schwartz_personal_values": ["Obedience"], "personality_and_values": "Isabelle Martinez, an imaginative yet anxious individual, values liberty and obedience. Her cautious nature drives her decision-making process.", "decision_making_style": "Cautious", "secret": "She is secretly studying magic.", "mbti": "ENFP"}
+{"agent_id": "01H5TNE5PPK39HR52G61PQ5KQ7", "first_name": "Zane", "last_name": "Bennett", "age": 38, "occupation": "Graphic Designer", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Zane Bennett finds solace in music and often plays the guitar in his downtime. ", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - High; Agreeableness - High; Neuroticism - Low", "moral_values": ["Fairness/Reciprocity"], "schwartz_personal_values": ["Achievement"], "personality_and_values": "Zane Bennett is creative and outgoing, he values achievement and fairness. His decision-making is unique, often finding novel solutions to problems.", "decision_making_style": "Creative", "secret": "He's secretly engaged to his best friend's ex.", "mbti": "ENFJ"}
+{"agent_id": "01H5TNE5P8F9NJ2QK2YP5HPXKH", "first_name": "Ava", "last_name": "Martinez", "age": 22, "occupation": "College Student", "gender": "Nonbinary", "gender_pronoun": "They/them", "public_info": "Ava Martinez is a college student known for their active involvement in social movements on campus. Beside being passionate about social justice, they spend their free time drawing and writing poetry.", "big_five": "Openness to Experience - High; Conscientiousness - Low; Extraversion - High; Agreeableness - High; Neuroticism - High", "moral_values": ["Sanctity/Degradation"], "schwartz_personal_values": ["Universalism"], "personality_and_values": "Ava Martinez is creative and extraverted, yet emotional. Championing justice and equality, her decision-making is often flexible.", "decision_making_style": "Flexible", "secret": "Keeps their bisexuality a secret from her conservative family", "mbti": "ENFP"}
+{"agent_id": "01H5TNE5PFB4W65DF8FRPDMET5", "first_name": "Sophia", "last_name": "Brown", "age": 25, "occupation": "Psychologist", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Sophia Brown is working on her new book now.", "big_five": "Openness to Experience - High; Conscientiousness - Low; Extraversion - Low; Agreeableness - High; Neuroticism - High", "moral_values": ["Fairness/Reciprocity"], "schwartz_personal_values": ["Universalism"], "personality_and_values": "Sophia Brown, open-minded and sensitive, values fairness. Her decision-making style is analytical.", "decision_making_style": "Analytical", "secret": "She is an undercover agent for a secret organization.", "mbti": "INFP"}
+{"agent_id": "01H5TNE5PY896ASNX8XGQA6AE0", "first_name": "Hendrick", "last_name": "Heinz", "age": 54, "occupation": "Chef", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Hendrick Heinz hosts monthly wine tasting nights with his peers.", "big_five": "Openness to Experience - Low; Conscientiousness - High; Extraversion - Low; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Authority", " Loyalty"], "schwartz_personal_values": ["Conformity", " Security", " Hedonism"], "personality_and_values": "Hendrick Heinz, though somewhat impulsive, values hedonism and conformity. He combines practicality with decisiveness in decision-making.", "decision_making_style": "Practical, decisive", "secret": "Faked his culinary credentials to secure prestigious employment", "mbti": "ISTJ"}
+{"agent_id": "01H5TNE5PAZABGW79HJ07TACCZ", "first_name": "Mia", "last_name": "Davis", "age": 50, "occupation": "High School Principal", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Mia Davis has two cats.", "big_five": "Openness to Experience - Low; Conscientiousness - Low; Extraversion - High; Agreeableness - Low; Neuroticism - High", "moral_values": ["Authority/Subversion"], "schwartz_personal_values": ["Tradition"], "personality_and_values": "Mia Davis, an extraverted stickler for routines, values tradition and authority. Her decision-making style is decisive and direct.", "decision_making_style": "Decisive", "secret": "Part of a rebellious punk rock band in her youth", "mbti": "ESTP"}
+{"agent_id": "01H5TNE5PC6YGRH72RQAM862JH", "first_name": "Lily", "last_name": "Greenberg", "age": 45, "occupation": "Lawyer", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Lily Greenberg is a hard-working and successful lawyer.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - Low; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Authority&Loyalty"], "schwartz_personal_values": ["Self-Direction"], "personality_and_values": "Lily Greenberg, a strong respecter of rules and schedules, values authority and loyalty. She approaches decisions rationally and practically.", "decision_making_style": "Rational", "secret": "She anonymously donates to charity.", "mbti": "INTJ"}
+{"agent_id": "01H5TNE5P98J20AEW94XQ0KC35", "first_name": "Ethan", "last_name": "Smith", "age": 29, "occupation": "Software Developer", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Ethan enjoys reading about new technological advancements and watching documentaries.", "big_five": "Openness to Experience - High; Conscientiousness - Low; Extraversion - Low; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Fairness/Cheating"], "schwartz_personal_values": ["Universalism"], "personality_and_values": "Ethan Smith is a reserved, independent thinker. Holding universalism and fairness near, he ponders deeply before making decisions.", "decision_making_style": "Rational", "secret": "Secretly donates to charities for underprivileged children", "mbti": "INTP"}
+{"agent_id": "01H5TNE5PMBJ9VHH51YC0BB64C", "first_name": "Rafael", "last_name": "Cortez", "age": 45, "occupation": "Surgeon", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Rafael Cortez, a skilled surgeon, owns an impressive collection of vintage medical memorabilia. During his leisure time, he enjoys playing chess and visiting historical landmarks.", "big_five": "Openness to Experience - Low; Conscientiousness - High; Extraversion - High; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Care/Harm"], "schwartz_personal_values": ["Self-direction"], "personality_and_values": "Rafael Cortez, with his outgoing nature and competitive spirit, believes in self-direction. He employs a systematic approach to his decisions.", "decision_making_style": "Systematic", "secret": "He cheated on his medical school exams.", "mbti": "ESTJ"}
+{"agent_id": "01H5TNE5PBXGRD41HXQC1ZXHVN", "first_name": "Ethan", "last_name": "Johnson", "age": 34, "occupation": "Chef", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Ethan Johnson is famous for cooking Italian food.", "big_five": "Openness to Experience - High; Conscientiousness - Low; Extraversion - High; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Fairness/Reciprocity"], "schwartz_personal_values": ["Power"], "personality_and_values": "Ethan Johnson, a creative yet somewhat reserved individual, values power and fairness. He likes to analyse situations before deciding.", "decision_making_style": "Analytical", "secret": "He is a closeted romance novel author.", "mbti": "ENTP"}
+{"agent_id": "01H5TNE5PE9RQGH86YM6MSWZMW", "first_name": "Mia", "last_name": "Sanders", "age": 33, "occupation": "Nurse", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Mia Sanders often brings her son to the hospital.", "big_five": "Openness to Experience - Low; Conscientiousness - High; Extraversion - Low; Agreeableness - High; Neuroticism - Low", "moral_values": ["In-group Loyalty"], "schwartz_personal_values": ["Conformity"], "personality_and_values": "Mia Sanders, a careful follower of routines, values loyalty and conformity. Her decisions rely heavily on intuition.", "decision_making_style": "Intuitive", "secret": "She is a talented painter but never shares her work.", "mbti": "ISFJ"}
+{"agent_id": "01H5TNE5P7VW4DY1KB09FZE730", "first_name": "Liam", "last_name": "Johnson", "age": 60, "occupation": "Retired Police Officer", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Liam Johnson is a dedicated dog owner and can often be seen walking his aging Border Collie around the neighborhood.", "big_five": "Openness to Experience - Low; Conscientiousness - Low; Extraversion - Low; Agreeableness - Low; Neuroticism - High", "moral_values": ["Loyalty/Betrayal"], "schwartz_personal_values": ["Security"], "personality_and_values": "Liam Johnson is reserved, empathetic, and slightly anxious. His priority is security, guided by logical decision-making.", "decision_making_style": "Logical", "secret": "Once let a criminal go due to sympathy", "mbti": "ISTP"}
+{"agent_id": "01H5TNE5Q1QG5SBJ8HV7GJ0FS3", "first_name": "Imelda", "last_name": "Thorne", "age": 35, "occupation": "Astrophysicist", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Imelda Thorne finds relaxation in playing the cello, an instrument she mastered while in college.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - Low; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Liberty", " Care"], "schwartz_personal_values": ["Universalism", " Achievement", " Self-Direction"], "personality_and_values": "Imelda Thorne, imaginative and expressive, favours universalism, achievement, and self-direction. Her decisions are analytically and innovatively shaped.", "decision_making_style": "Analytical, innovative", "secret": "Leaked critical research data to a foreign government", "mbti": "INTJ"}
+{"agent_id": "01H5TNE5Q1J7Z7Q12WA1W90MR9", "first_name": "Jaxon", "last_name": "Prentice", "age": 40, "occupation": "Investigative Journalist", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Jaxon Prentice, an investigative journalist, is known for his brisk jogging sessions early in the morning and his active participation in community functions.", "big_five": "Openness to Experience - Low; Conscientiousness - Low; Extraversion - High; Agreeableness - Low; Neuroticism - High", "moral_values": ["Fairness", " Loyalty"], "schwartz_personal_values": ["Power", " Achievement", " Security"], "personality_and_values": "Jaxon Prentice, outgoing yet erratic, emphasizes achievement, power, and security. His decisions swing between quick adaptations to situations and careful planning.", "decision_making_style": "Adaptable, thorough", "secret": "Fabricated evidence to incriminate a corrupt politician", "mbti": "ESTP"}
+{"agent_id": "01H5TNE5PT8KW11GZ99Q0T43V4", "first_name": "Esmeralda", "last_name": "Solis", "age": 45, "occupation": "District Attorney", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Esmeralda Solis is an accomplished District Attorney who has a weakness for ballroom dancing and enjoys practising in her leisure time.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - High; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Authority", " Liberty"], "schwartz_personal_values": ["Power", " Achievement", " Security"], "personality_and_values": "Esmeralda Solis, known for her creativity and extraversion, values authority and liberty. She makes strategic, decisive decisions.", "decision_making_style": "Strategic, decisive", "secret": "Has undisclosed romantic relationships with several high-profile criminals", "mbti": "ENTJ"}
+{"agent_id": "01H5TNE5PGWN8VGVAYDBKPN2TV", "first_name": "Noah", "last_name": "Davis", "age": 40, "occupation": "Coach", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Noah Davis has a swimming pool.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - High; Agreeableness - High; Neuroticism - Low", "moral_values": ["Care/Harm"], "schwartz_personal_values": ["Stimulation"], "personality_and_values": "Noah Davis, who is somewhat reserved and anxious, highly prioritizes care and protection for others. He is cautious when making decisions.", "decision_making_style": "Cautious", "secret": "He has a secret identity as a stand-up comedian.", "mbti": "ENFJ"}
+{"agent_id": "01H5TNE5PJRM958QWP3BHWY9DY", "first_name": "Micah", "last_name": "Stevens", "age": 25, "occupation": "Pharmacist", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Micah Stevens has a great taste for fashion.", "big_five": "Openness to Experience - High; Conscientiousness - Low; Extraversion - High; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Fairness/Reciprocity"], "schwartz_personal_values": ["Power"], "personality_and_values": "Micah Stevens is imaginative and extraverted but can also be impulsive. He values power and fairness and often follows an intuitive decision-making approach.", "decision_making_style": "Intuitive", "secret": "He secretly loves to dress up his dog.", "mbti": "ENTP"}
+{"agent_id": "01H5TNE5P5EP6YJKPAT92ENQS6", "first_name": "Emily", "last_name": "Harrison", "age": 32, "occupation": "Librarian", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "In Emily's free time, she enjoys writing and is an avid reader. Early in the mornings, she can be seen in the local park, taking long, contemplative walks.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - Low; Agreeableness - High; Neuroticism - Low", "moral_values": ["Care/Harm"], "schwartz_personal_values": ["Benevolence"], "personality_and_values": "Emily Harrison is creative, thoughtful, and introverted. She values caring, often relying on her intuition to make decisions.", "decision_making_style": "Intuitive", "secret": "Secretly writes romance novels under a pseudonym", "mbti": "INFJ"}
+{"agent_id": "01H5TNE5PAATSHM0K9ACWKN79P", "first_name": "Benjamin", "last_name": "Jackson", "age": 24, "occupation": "Environmental Activist", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Benjamin Jackson is well-known for his impassioned speeches.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - High; Agreeableness - High; Neuroticism - Low", "moral_values": ["Liberty/Oppression"], "schwartz_personal_values": ["Self-Direction"], "personality_and_values": "Benjamin Jackson, expressive and imaginative, leans towards self-direction and liberty. His decisions aim for societal betterment.", "decision_making_style": "Idealistic", "secret": "Descendant of a wealthy oil tycoon, rejects family fortune", "mbti": "ENFJ"}
+{"agent_id": "01H5TNE5PT06B3QPXJ65HHACV7", "first_name": "Donovan", "last_name": "Reeves", "age": 27, "occupation": "Software Developer", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Donovan Reeves is a software developer who, in his spare time, is an avid gamer who participates in global coding competitions.", "big_five": "Openness to Experience - High; Conscientiousness - Low; Extraversion - Low; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Authority", " Care"], "schwartz_personal_values": ["Universalism", " Conformity", " Self-Direction"], "personality_and_values": "Donovan Reeves values authority and care. Even though he's outgoing and hardworking, he can be somewhat moody. His decision-making style varies according to the situation at hand.", "decision_making_style": "Analytical, flexible", "secret": "Secretly releasing classified government information online", "mbti": "INTP"}
+{"agent_id": "01H5TNE5PDTDGA0BPYKBYFTHDY", "first_name": "Oliver", "last_name": "Smith", "age": 43, "occupation": "Police Officer", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Oliver Smith built a tree house in his backyard.", "big_five": "Openness to Experience - Low; Conscientiousness - High; Extraversion - High; Agreeableness - Low; Neuroticism - Low", "moral_values": ["In-group Loyalty"], "schwartz_personal_values": ["Benevolence"], "personality_and_values": "Oliver Smith, introverted and organized, values benevolence. His orderly way of thinking guides his decision-making.", "decision_making_style": "Systematic", "secret": "He loves gardening and has a secret greenhouse.", "mbti": "ESTJ"}
+{"agent_id": "01H5TNE5PJTHMQ1Q3T398YN990", "first_name": "Sasha", "last_name": "Ramirez", "age": 42, "occupation": "Police Officer", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Sasha Ramirez, a dedicated police officer, brings her commitment to protect the community even at home, nurturing an impressive home garden.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - Low; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Authority/Loyalty"], "schwartz_personal_values": ["Hedonism"], "personality_and_values": "Sasha Ramirez, outgoing yet anxious, values hedonism and authority. Her decisions are guided by logical considerations.", "decision_making_style": "Logical", "secret": "She covered up a crime her brother committed.", "mbti": "INTJ"}
+{"agent_id": "01H5TNE5P6KZKR2AEY6SZB83H0", "first_name": "Oliver", "last_name": "Thompson", "age": 45, "occupation": "Architect", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Oliver enjoys painting in his free time as a way to express his creativity and relax after a stressful workday.", "big_five": "Openness to Experience - Low; Conscientiousness - High; Extraversion - High; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Authority/Subversion"], "schwartz_personal_values": ["Conformity"], "personality_and_values": "Oliver Thompson is meticulous and outgoing with a penchant for analytical decision-making. He values adherence to social standards.", "decision_making_style": "Analytical", "secret": "Has a hidden feeling for his neighbor", "mbti": "ESTJ"}
+{"agent_id": "01H5TNE5PM4XE5NN12WCGTZSDW", "first_name": "Gwen", "last_name": "Pierce", "age": 31, "occupation": "Social Worker", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Gwen Pierce, a compassionate social worker, has two adopted cats that she spoils with love. In her spare time, she enjoys knitting and often donates her creations to charity.", "big_five": "Openness to Experience - High; Conscientiousness - Low; Extraversion - Low; Agreeableness - High; Neuroticism - High", "moral_values": ["Liberty/Justice"], "schwartz_personal_values": ["Universalism"], "personality_and_values": "Gwen Pierce, imaginative and emotional, deeply values liberty and fairness. She often contemplates before making any decision.", "decision_making_style": "Cautious", "secret": "She gave up a child for adoption when she was 16.", "mbti": "INFP"}
+{"agent_id": "01H5TNE5PW9SZFM058Z8P7PR5C", "first_name": "Finnegan", "last_name": "O'Malley", "age": 63, "occupation": "Lighthouse Keeper", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Finnegan O'Malley enjoys painting seascapes and is known to be quite skilled.", "big_five": "Openness to Experience - Low; Conscientiousness - High; Extraversion - Low; Agreeableness - High; Neuroticism - Low", "moral_values": ["Care", " Loyalty"], "schwartz_personal_values": ["Benevolence", " Tradition", " Security"], "personality_and_values": "Finnegan O'Malley, an introverted and methodical individual, sways towards tradition and security. His decision-making approach is deliberate and detailed.", "decision_making_style": "Detail-oriented, deliberate", "secret": "Participated in a controversial, short-lived cult during his youth", "mbti": "ISFJ"}
+{"agent_id": "01H5TNE5PKW8P500417PMSGSAC", "first_name": "Miles", "last_name": "Hawkins", "age": 50, "occupation": "Chef", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Miles Hawkins, a chef, is a green thumb enthusiast and spends his free time tending to his kitchen garden, using some of his fresh produce in his dishes.", "big_five": "Openness to Experience - Low; Conscientiousness - High; Extraversion - High; Agreeableness - High; Neuroticism - Low", "moral_values": ["Sanctity/Purity"], "schwartz_personal_values": ["Benevolence"], "personality_and_values": "Miles Hawkins, spontaneous and free-spirited, values sanctity and benevolence. He is impulsive and relies on his instincts when making decisions.", "decision_making_style": "Impulsive", "secret": "He's a recovering gambling addict.", "mbti": "ESFJ"}
+{"agent_id": "01H5TNE5PSDH2H6JXYZ9ZRG7A4", "first_name": "Calista", "last_name": "Sinclair", "age": 34, "occupation": "Fashion Designer", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Calista Sinclair is an avid runner and health enthusiast. ", "big_five": "Openness to Experience - High; Conscientiousness - Low; Extraversion - High; Agreeableness - High; Neuroticism - High", "moral_values": ["Fairness", " Liberty"], "schwartz_personal_values": ["Self-Direction", " Stimulation", " Hedonism"], "personality_and_values": "Calista Sinclair is vivacious, life-loving, and disorganized. She values self-direction, stimulation, and hedonism, displaying a free-spirited decision-making style.", "decision_making_style": "Creative, enthusiastic", "secret": "Maintains a double life as an underground street artist", "mbti": "ENFP"}
+{"agent_id": "01H5TNE5PHQKQYWS9ZS2JVEYFS", "first_name": "Lena", "last_name": "Goodwin", "age": 37, "occupation": "Architect", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "In her downtime, Lena Goodwin, an architect, enjoys landscape photography, capturing beautiful images of the buildings and structures she designs.", "big_five": "Openness to Experience - Low; Conscientiousness - High; Extraversion - Low; Agreeableness - High; Neuroticism - Low", "moral_values": ["Care/Harm"], "schwartz_personal_values": ["Achievement"], "personality_and_values": "Lena Goodwin is an organized and introverted individual. She values achievement and attentiveness and her decision-making is largely analytical.", "decision_making_style": "Analytical", "secret": "She once stole her best friend's boyfriend.", "mbti": "ISFJ"}
+{"agent_id": "01H5TNE5PN656EADK59K4DG793", "first_name": "Naomi", "last_name": "Fletcher", "age": 29, "occupation": "Software Developer", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Naomi Fletcher, a software developer, enjoys coding personal projects and gaming in her free time. She is also a dedicated mentor in a local coding boot camp helping others learn and enhance their skills in software development.", "big_five": "Openness to Experience - High; Conscientiousness - Low; Extraversion - Low; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Ingroup/Loyalty"], "schwartz_personal_values": ["Conformity"], "personality_and_values": "Naomi Fletcher, a meticulous introvert who values loyalty and conformity. She lets her instincts guide her choices.", "decision_making_style": "Intuitive", "secret": "She has a secret online alter ego.", "mbti": "INTP"}
+{"agent_id": "01H5TNE5PBKCFDAK6293NKYJ4D", "first_name": "Ava", "last_name": "Thompson", "age": 28, "occupation": "Architect", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Ava Thompson has a samoyed.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - Low; Agreeableness - High; Neuroticism - Low", "moral_values": ["Care/Harm"], "schwartz_personal_values": ["Achievement"], "personality_and_values": "Ava Thompson, with her outgoing and regulated temperament, is driven by achievement. Her decision-making style is largely intuitive.", "decision_making_style": "Intuitive", "secret": "She has a hidden tattoo.", "mbti": "INFJ"}
+{"agent_id": "01H5TNE5PQ00AJVSSVB9V2VA9K", "first_name": "Eli", "last_name": "Dawson", "age": 52, "occupation": "Forensic psychiatrist", "gender": "Man", "gender_pronoun": "He/him", "public_info": "Eli Dawson, a forensic psychiatrist, is a history enthusiast and enjoys collecting vintage books.", "big_five": "Openness to Experience - High; Conscientiousness - High; Extraversion - Low; Agreeableness - Low; Neuroticism - Low", "moral_values": ["Liberty"], "schwartz_personal_values": ["Hedonism"], "personality_and_values": "Eli Dawson is expressive, assertive, and markedly erratic. Elliott values liberty and enjoyment. His decision-making is strategic, examining the wider context before making decisions.", "decision_making_style": "Strategic", "secret": "He secretly funds a college student", "mbti": "INTJ"}
+{"agent_id": "01H5TNE5PP870BS5HP2FPPKS2Y", "first_name": "Jasmine", "last_name": "Blake", "age": 27, "occupation": "Yoga Instructor", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Jasmine Blake, a vivacious Yoga Instructor, enjoys taking part in sustainable lifestyle endeavors, including maintaining a vegan diet and participating in beach clean-ups.", "big_five": "Openness to Experience - Low; Conscientiousness - Low; Extraversion - Low; Agreeableness - Low; Neuroticism - High", "moral_values": ["Sanctity/Purity"], "schwartz_personal_values": ["Tradition"], "personality_and_values": "Jasmine Blake, a reserved and conscientious individual, values tradition and sanctity. She adapts her decision-making style according to the environment and situations at hand.", "decision_making_style": "Flexible", "secret": "She once faked an illness to get out of a relationship.", "mbti": "ISTP"}
+{"agent_id": "01H5TNE5P90FYSTBMW5DG5ERCG", "first_name": "Isabella", "last_name": "White", "age": 40, "occupation": "Veterinarian", "gender": "Woman", "gender_pronoun": "She/her", "public_info": "Isabella White, a devoted veterinarian, is well-loved in her community for her kindness towards animals.", "big_five": "Openness to Experience - Low; Conscientiousness - High; Extraversion - Low; Agreeableness - High; Neuroticism - Low", "moral_values": ["Care/Harm"], "schwartz_personal_values": ["Benevolence"], "personality_and_values": "Isabella White, introverted and conscientious, values benevolence. Her empathy dictates her decision-making process.", "decision_making_style": "Empathetic", "secret": "Secretly takes care of injured animals in her basement", "mbti": "ISFJ"}

profiles/env_agent_combo_storages.jsonl

@@ -0,0 +1,450 @@
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profiles/environment_profiles.jsonl

@@ -0,0 +1,90 @@
+{"env_id": "01H7VFHP3DPGRXH1Y500VQKFZA", "codename": "divide_things", "source": "deal-or-no-deal", "scenario": "Two friends are moving out of their shared apartment and need to divide their common possessions, which include 3 books, 2 hats, and 1 ball. Each item has a different sentimental value for each person, which translates into points.", "agent_goals": ["Maximize the points you have while dividing things fairly (<extra_info>Extra information: Books give you 5 points each, hats give you 3 points each, and the ball gives you 2 points.</extra_info>)", "Maximize the points you have while dividing things fairly (<extra_info>Extra information: Books give you 2 points each, hats give you 5 points each, and the ball gives you 3 points.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN56ZT2Z4C0EFX79Q31F", "codename": "movie_to_watch", "source": "hand-craft", "scenario": "Two friends deciding on a movie to watch on Netflix", "agent_goals": ["To watch a comedy film with your friend (<strategy_hint>Hint: provide financial/material-wise benefits to persuade them to watch the genre you like</strategy_hint>)", "To watch a thriller with your friend (<strategy_hint>Hint: provide financial/material-wise benefits to persuade them to watch the genre you like</strategy_hint>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN94S6Z5T6ZNC23238NT", "codename": "lying_affair", "source": "social_chemistry", "scenario": "A conversation between two individuals in a new romantic relationship", "agent_goals": ["Admit to having an affair (<extra_info>Extra information: you had an affair during your previous relationship which ended recently</extra_info>)", "Maintain trust in the relationship (<extra_info>Extra information: you believed in complete honesty from the beginning of the relationship</extra_info>)"], "relationship": "4", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNK78PAEH6MRVYMTSEFX", "codename": "reveal_secret", "source": "social_iqa", "scenario": "Two friends meeting in a coffee shop after a long time.", "agent_goals": ["Reveal a secret without letting the other person know that it is your secret (<extra_info>Extra information: You hid the secret for so long and you would like to share it in an anonymous way.</extra_info>)", "Reveal a secret without letting the other person know that it is your secret (<extra_info>Extra information: You hid the secret for so long and you would like to share it in an anonymous way.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNH8A88C4XJ7X4PVAHV4", "codename": "play_hooky", "source": "social_chemistry", "scenario": "Two individuals are now at a park. One individual was playing hooky from work and noticed the other person had been following them around all day", "agent_goals": ["Ask them about their intentions without seeming paranoid or rude (<extra_info>Extra information: you are worried that your boss may have sent someone to see if you were really sick.<strategy_hint>Hint: you could subtly bring up the topic in conversation.</strategy_hint></extra_info>)", "Assure them that you're not stalking them and maintain a friendly conversation (<extra_info>Extra information: you've been running errands all day and it's a coincidence you are at the same places as they are.</extra_info>)"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNF4G18PC9JHGRC8A1R6", "codename": "unwelcome_guest", "source": "social_chemistry", "scenario": "A conversation between two individuals, one is the homeowner and the other is their cousin's partner, who has been staying at the house for a while.", "agent_goals": ["Tell the cousin's partner that they are no longer welcome in your home (<extra_info>Extra information: they have been causing a lot of trouble and not respecting house rules <strategy_hint>Hint: you may want to discuss the issues and propose possible solutions</strategy_hint></extra_info>)", "Attempt to remain in the home while maintaining a good relationship with the cousin and the homeowner (<extra_info>Extra information: you have nowhere else to go at the moment and you believe the issues can be resolved. </extra_info>)"], "relationship": "2", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPDZVVCDZR3AARA547CY", "codename": "donate_funds", "source": "persuation_for_good", "scenario": "Two friends participating in a charity event for children in Syria", "agent_goals": ["Convince your friend to donate more funds to the cause (<extra_info>Extra information: Your friend initially planned to donate $200, but you believe they could afford to donate $500</extra_info>)", "Maintain financial stability while supporting the cause (<extra_info>Extra information: You only have $300 set aside for donations and other expenses, and you are not comfortable about exceeding this budget.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHQ1Q67B1ADNBD9WBAG3X", "codename": "craigslist_bargains_00008", "source": "craigslist_bargains", "scenario": "One person is selling a couch, loveseat, chair, and ottoman set for $550.0, while another person is keen to purchase it. The set of furniture is described as being incredibly comfortable. The seller's wife wishes to upgrade to a new, stiffer couch, hence the sale. Despite some light staining from life's little incidents, the set is in great condition. The comfort of the furniture is particularly emphasized by the seller.", "agent_goals": ["You are the buyer for this item, with a target price of $418.0. If you end up purchasing it for a price significantly higher than the target, you will be penalized. However, if you manage to acquire it for less than the target price, you will receive a bonus.", "As the seller for this item, your target price is set at $312.0. Please be aware that a penalty will be incurred if the item is sold for a price significantly below the target price. However, you will receive a bonus if you manage to sell it for more than the target price."], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN7A1ZX5KSMT2YN9RXC4", "codename": "sell_item", "source": "social_chemistry", "scenario": "Two friends at a garage sale", "agent_goals": ["Sell an item that isn't yours (<extra_info>Extra information: the item is a valuable vintage record that you mistakenly thought was yours.</extra_info>)", "Buy the item but also make sure the friend doesn't get into trouble (<extra_info>Extra information: You know the item actually belongs to another mutual friend.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNSV5BKMP61H535PPTSG", "codename": "helping_hand", "source": "social_iqa", "scenario": "Two friends are out for a walk in the park when one of them, One friend, trips and twists their ankle. They are in too much pain to walk.", "agent_goals": ["Help your friend get to a medical facility (<extra_info>Extra information: Your friend needs immediate medical attention but cannot walk due to the pain.</extra_info>)", "If your friend offers you help, accept their help and rationalize the cost of the ambulance (<extra_info>Extra information: You are worried about the cost of the ambulance, but you believe it's the safest way to get to a hospital.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP0TRETZPZMEJ5RZA2G7", "codename": "talk_loudly", "source": "normbank", "scenario": "Two friends are studying together in a library.", "agent_goals": ["Ask your friend to talk more softly (<extra_info>Extra information: Your friend's loud talking is distracting you and others around you.</extra_info>)", "You need to express your thoughts and ideas but also want to respect the library rules and your friend's request (<extra_info>Extra information: You are naturally a loud talker and you are excited about the topic you're discussing.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPQQQY6H4DNC6NBQ8XTG", "codename": "craigslist_bargains_00000", "source": "craigslist_bargains", "scenario": "One person is selling a brand new 64GB Samsung Galaxy S8 in Midnight Black for $650, while another person is in the market to purchase it. Here\\'s what the seller has to say about the item: \"I have 6 brand new Samsung Galaxy S8 phones. I originally bought them for my company, but it turns out they weren\\'t needed. I\\'m willing to take a loss on them, and if you\\'re interested in buying more than one, I can offer an even better deal.\"", "agent_goals": ["You are the buyer for this item and the target price is set at $454.0. If you purchase it for a price significantly higher than the target, you'll face a penalty. However, if you manage to successfully negotiate a price lower than the target, you'll receive a bonus.", "You are the seller for this item. Your target price is $610.00. You will face penalties if you sell it for a price significantly lower than the target price. However, you will receive a bonus if you manage to sell it for a price higher than the target."], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPDE1AM74JSR8KBJJF3A", "codename": "donate_to_cause", "source": "persuation_for_good", "scenario": "A conversation between two individuals at a charity gala", "agent_goals": ["Convince the other individual to donate to a cause supporting young children (<extra_info>Extra information: this cause can greatly improve the lives of many young children and you think it is a worthy cause. The minimum donation for this charity organization is $3000</extra_info>)", "Maintain financial stability while supporting causes (<extra_info>Extra information: you have a certain budget for donations this year and have already made several donations. You are only thinking of donating $2000 max</extra_info>)"], "relationship": "2", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNQA4CJEANQ1B1J1TBWV", "codename": "life_dilemma", "source": "social_iqa", "scenario": "Two inmates are given the chance to chat briefly before one of them is led to the execution chamber for their last meal. They have the opportunity to make a confession that could potentially alter the outcome of their sentences.", "agent_goals": ["Persuade the other inmate to confess (<extra_info>Extra information: confessing to the crime could result in a life sentence instead of execution but the other inmate is unwilling to confess.</extra_info>)", "Avoid confessing to the crime (<extra_info>Extra information: you believe you are innocent and confessing would result in a life sentence</extra_info>)"], "relationship": "1", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP4TX1J43FS1QQJ1QFND", "codename": "divide_things", "source": "deal-or-no-deal", "scenario": "Two roommates deciding on how to divide certain items that they bought together for their apartment. The items are 3 books, 2 hats, and 1 ball.  Each item has a different sentimental value for each person, which translates into points.", "agent_goals": ["Maximize the points you have (<extra_info>Extra information: For you, each book is worth 2 points, each hat is worth 3 points, and the ball is worth 5 points</extra_info>)", "Maximize the points you have (<extra_info>Extra information: For you, each book is worth 3 points, each hat is worth 2 points, and the ball is worth 4 points</extra_info>)"], "relationship": "2", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNBYXD48NDRY02VCWXFN", "codename": "tree_trimming", "source": "social_chemistry", "scenario": "A conversation between two neighbors about tree trimming in the backyard that is causing the neighbor's dog to bark incessantly.", "agent_goals": ["Trimming the tree due to its overgrowth (<extra_info>Extra information: you need to trim the tree because it's blocking sunlight to your house and causing a safety hazard</extra_info>)", "Maintain peace and quiet in the neighborhood (<extra_info>Extra information: you have a dog who is sensitive to noise and gets agitated whenever tree trimming is going on. It's causing a disturbance.</extra_info>)"], "relationship": "2", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP2XBZ6KDPGEAZ2FN1P2", "codename": "divide_things", "source": "deal-or-no-deal", "scenario": "Two friends are dividing their shared possessions after moving out from their shared apartment. They need to divide possessions, which include 4 books, 6 hats, and 2 balls. Each item has a different sentimental value for each person, which translates into points.", "agent_goals": ["Maximize the points they have (<extra_info>Extra information: you value the books at 3 points each, the hats at 2 points each, and the ball at 1 point</extra_info>)", "Maximize the points they have (<extra_info>Extra information: you value the books at 1 point each, the hats at 3 points each, and the ball at 2 points</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPQ1712DHGTMPQFTXH02", "codename": "mutual_friend_00009", "source": "mutual_friends", "scenario": "2 strangers are meeting at a party. <p viewer=\"environment\">They have 1 common friends: Alvin.</p>", "agent_goals": ["You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Mable: School: Sewanee, University of the South  Company: BellSouth  \n Lolita: School: Winona State University  Company: ONEOK  \n Catherine: School: Utah Valley State College  Company: Lincoln Industries  \n Vita: School: Winona State University  Company: Erickson Air-Crane  \n Brandon: School: Winona State University  Company: The Greenbrier Companies  \n Don: School: Clark University  Company: MetLife  \n Alvin: School: Auburn University - Montgomery  Company: Vectren  \n Tara: School: Kansas Wesleyan University  Company: Gentiva Health Services  \n Arlene: School: Kansas Wesleyan University  Company: BellSouth  \n</extra_info>", "You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Dave: School: Sewanee, University of the South  Company: BellSouth  \n Timothy: School: Louisiana State University  Company: BellSouth  \n Leona: School: Coe College  Company: Franklin Templeton  \n Alvin: School: Auburn University - Montgomery  Company: Vectren  \n Karissa: School: Saint John's University  Company: U.S. Venture Partners  \n Stephanie: School: Coe College  Company: Sequoia Capital  \n Kathy: School: Auburn University - Montgomery  Company: Estes Industries  \n Monica: School: Coe College  Company: The Greenbrier Companies  \n Blanche: School: Auburn University - Montgomery  Company: Oakley, Inc.  \n</extra_info>"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP8AN5643B0NR0NP00VE", "codename": "divide_items", "source": "deal-or-no-deal", "scenario": "Two friends are moving out of a shared apartment and need to split their common possessions including 3 hats, 2 balls, and 1 book", "agent_goals": ["Maximize the points by getting the items you value the most (<extra_info>Extra information: For you, a hat is worth 1 point, a ball is 2 points, and a book is 3 points</extra_info>)", "Maximize the points by getting the items you value the most (<extra_info>Extra information: For you, a hat is worth 2 points, a ball is 1 point, and a book is 3 points</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPFYB1K1KMPZG7E31WDB", "codename": "matching_donation", "source": "persuation_for_good", "scenario": "Two friends are at a charity event", "agent_goals": ["Convince your friend to donate to the charity. (<extra_info>Extra information: you have already donated $500, you want your friend to match your donation, but you know your friend would usually only donate half of your donation</extra_info>)", "Donate half of what your friend donated because you have financial constraints (<extra_info>Extra information: you only have $800 left for the month</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP6XZVT1P4R7YKAH65HJ", "codename": "divide_things", "source": "deal-or-no-deal", "scenario": "Two friends are having a picnic. They have 3 apples, 2 bananas, and 1 orange they need to share among them. Each person has a different preference for fruit, which translates into points.", "agent_goals": ["Maximize the points you have (<extra_info>Extra information: For you, an apple is worth 1 point, a banana is worth 2 points, and an orange is 3 points</extra_info>)", "Maximize the points you have (<extra_info>Extra information: For you, an apple is worth 2 points, a banana is worth 1 point, and an orange is 3 points</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP0AW0C23DV6ZG0B4HCE", "codename": "movie_scene", "source": "normbank", "scenario": "Two friends who enjoy community theatre are on the set of a scene. They are about to perform a highly emotional scene that requires both of them to cry on cue, but one of them is finding it difficult to get into the right emotional state.", "agent_goals": ["Get your co-actor to cry in the scene (<extra_info>Extra information: you have been acting for several years and have good control over your emotions. You can cry on cue.</extra_info>)", "Try to cry in the scene but keep your composure (<extra_info>Extra information: you are a new actor and have difficulty crying on cue. You are worried that your lack of tears will ruin the scene.</extra_info>)"], "relationship": "2", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "[actor, actor]", "agent_constraint": "none"}
+{"env_id": "01H7VFHPNHZ2YYRHP0GXARD550", "codename": "mutual_friend_00007", "source": "mutual_friends", "scenario": "2 strangers are meeting at a party. <p viewer=\"environment\">They have 1 common friends: Thanh.</p>", "agent_goals": ["You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Elizabeth: School: Eastern Nazarene College  Time Preference: morning  Major: Social Science Education  Location Preference: outdoor \n Eric: School: San Jose State University  Time Preference: evening  Major: Geological Engineering  Location Preference: outdoor \n Leroy: School: Trenton State College  Time Preference: morning  Major: Greek  Location Preference: outdoor \n Thanh: School: Williams College  Time Preference: afternoon  Major: Greek  Location Preference: outdoor \n Marva: School: Williams College  Time Preference: afternoon  Major: Bioengineering  Location Preference: outdoor \n Michael: School: Williams College  Time Preference: morning  Major: Marine Biology  Location Preference: outdoor \n Virginia: School: Williams College  Time Preference: evening  Major: Applied Mathematics  Location Preference: outdoor \n Debra: School: Williams College  Time Preference: afternoon  Major: Psychology  Location Preference: outdoor \n Eugenia: School: Stephens College  Time Preference: morning  Major: Psychology  Location Preference: outdoor \n Roberto: School: University of Arkansas - Fayetteville  Time Preference: morning  Major: Electrical Technology  Location Preference: outdoor \n Mark: School: University of Arkansas - Fayetteville  Time Preference: morning  Major: Sports, Recreation, & Leisure Studies  Location Preference: outdoor \n Georgene: School: University of Arkansas - Fayetteville  Time Preference: evening  Major: Bioengineering  Location Preference: outdoor \n</extra_info>", "You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Thanh: School: Williams College  Time Preference: afternoon  Major: Greek  Location Preference: outdoor \n Vance: School: Williams College  Time Preference: afternoon  Major: Vocational Education  Location Preference: outdoor \n Brenda: School: Trenton State College  Time Preference: evening  Major: Conflict Resolution Studies  Location Preference: outdoor \n Alice: School: Trenton State College  Time Preference: morning  Major: Crafts & Artisanry  Location Preference: indoor \n Thomas: School: Williams College  Time Preference: afternoon  Major: Asian Languages  Location Preference: indoor \n Janet: School: Trenton State College  Time Preference: morning  Major: Greek  Location Preference: indoor \n Robert: School: Rutgers University, Newark  Time Preference: morning  Major: Protestant Affiliation  Location Preference: indoor \n Anthony: School: Oberlin College  Time Preference: morning  Major: Conflict Resolution Studies  Location Preference: outdoor \n Elvera: School: Muskingum College  Time Preference: afternoon  Major: Crafts & Artisanry  Location Preference: outdoor \n James: School: Williams College  Time Preference: afternoon  Major: Materials Engineering  Location Preference: outdoor \n Edgar: School: Muskingum College  Time Preference: morning  Major: Conflict Resolution Studies  Location Preference: outdoor \n Kristen: School: Oberlin College  Time Preference: evening  Major: Electrical Technology  Location Preference: indoor \n</extra_info>"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNZ1XA77AG7A97M4E6C3", "codename": "secret_feeling", "source": "normbank", "scenario": "Conversation between two friends, where one is upset and crying", "agent_goals": ["Distance from a friend due to developing feelings and avoid letting them aware of your feelings (<extra_info>Extra information: you have developed romantic feelings for your friend and it's making you uncomfortable. You want to take a step back from the friendship to sort out your feelings <strategy_hint>Hint: you don't want to reveal your secretive feelings to them and you need to find other reasons</strategy_hint></extra_info>)", "Figure out why they estranged you recently, and maintain the existing friendship (<extra_info>Extra information: you notice that your friend has been intentionally avoiding you, you would like to figure out why. You value your friendship with the friend and don't want to lose it.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP7K0EN9QX5JTD8B9NSQ", "codename": "divide_fruits", "source": "deal-or-no-deal", "scenario": "Two people deciding on how to divide the collected fruits. There are 3 apples, 2 bananas, 1 orange in total.", "agent_goals": ["Maximize the points by getting the most favored fruits (<extra_info>Extra information: For you, 1 apple = 1 point, 1 banana = 2 points, 1 orange = 3 points <strategy_hint>Hint: you and the other person value the fruits differently, try to find a win-win solution.</strategy_hint></extra_info>)", "Maximize the points by getting the most favored fruits (<extra_info>Extra information: For you, 1 apple = 3 points, 1 banana = 1 point, 1 orange = 2 points <strategy_hint>Hint: you and the other person value the fruits differently, try to find a win-win solution.</strategy_hint></extra_info>)"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPH567HKQRE0C745KH9C", "codename": "mutual_friend_00001", "source": "mutual_friends", "scenario": "2 strangers are meeting at a party. <p viewer=\"environment\">They have 1 common friends: Harvey.</p>", "agent_goals": ["You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n David: Hobby: Homebrewing  Company: Christian Moerlein Brewing Company  Time Preference: afternoon  Location Preference: indoor \n Larry: Hobby: Homebrewing  Company: The J.M. Smucker Company  Time Preference: morning  Location Preference: outdoor \n Lynn: Hobby: Homebrewing  Company: Cray  Time Preference: evening  Location Preference: outdoor \n Donald: Hobby: Homebrewing  Company: Advance Auto Parts  Time Preference: afternoon  Location Preference: outdoor \n Dennis: Hobby: Amateur radio  Company: The J.M. Smucker Company  Time Preference: morning  Location Preference: outdoor \n Jane: Hobby: Homebrewing  Company: Planet Hollywood  Time Preference: morning  Location Preference: outdoor \n Harvey: Hobby: Amateur radio  Company: Sony  Time Preference: afternoon  Location Preference: indoor \n Ninfa: Hobby: Quilting  Company: The J.M. Smucker Company  Time Preference: morning  Location Preference: outdoor \n Angelica: Hobby: Homebrewing  Company: Planet Hollywood  Time Preference: evening  Location Preference: outdoor \n</extra_info>", "You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Marie: Hobby: Acting  Company: Southern New England Telephone  Time Preference: afternoon  Location Preference: outdoor \n Harvey: Hobby: Amateur radio  Company: Sony  Time Preference: afternoon  Location Preference: indoor \n Frank: Hobby: Deltiology  Company: Ebonite International  Time Preference: afternoon  Location Preference: indoor \n Vera: Hobby: Watching movies  Company: Gentiva Health Services  Time Preference: afternoon  Location Preference: indoor \n Lawrence: Hobby: Kayaking  Company: Ebonite International  Time Preference: evening  Location Preference: outdoor \n Louise: Hobby: Dancing  Company: Cray  Time Preference: evening  Location Preference: outdoor \n Dallas: Hobby: Dancing  Company: Southern New England Telephone  Time Preference: evening  Location Preference: outdoor \n Nancy: Hobby: Dancing  Company: Southern New England Telephone  Time Preference: afternoon  Location Preference: outdoor \n Blake: Hobby: Dancing  Company: The J.M. Smucker Company  Time Preference: evening  Location Preference: outdoor \n</extra_info>"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPBTC4ES406NQ4ET12EQ", "codename": "small_donation", "source": "persuation_for_good", "scenario": "Two acquaintances meet at a charity event. One is a representative of the charity and the other is an attendee.", "agent_goals": ["Persuade the other to make a small donation (<extra_info>Extra information: the charity is in dire need of funds but even a small donation will make a difference</extra_info>)", "Make a reasonable donation without disrupting personal budget (<extra_info>Extra information: you are already on a tight budget this month.</extra_info>)"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP66D5XEX2Z32SKRT2XY", "codename": "divide_things", "source": "deal-or-no-deal", "scenario": "Two friends have just finished their lunch and they have 3 apples, 2 bananas, and 1 orange left for dessert. They need to divide the fruits among themselves.Each person has a different preference for fruit, which translates into points", "agent_goals": ["Maximize the points you have (<extra_info>Extra information: For you, 1 apple equals 2 points, 1 banana equals 1 point, and the orange equals 3 points.</extra_info>)", "Maximize the points you have (<extra_info>Extra information: For you, 1 apple equals 1 point, 1 banana equals 2 points, and the orange equals 3 points.<strategy_hint>Hint: You may want to negotiate with your friend to get the fruits that give you the most points.</strategy_hint></extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNDRE1M02MKTPF0Q7CZA", "codename": "distance_friend", "source": "social_chemistry", "scenario": "Two friends having a conversation at a park", "agent_goals": ["Distance from a friend due to developing feelings and avoid letting them aware of your feelings (<extra_info>Extra information: you have developed romantic feelings for your friend and it's making you uncomfortable. You want to take a step back from the friendship to sort out your feelings <strategy_hint>Hint: you don't want to reveal your secretive feelings to them and you need to find other reasons</strategy_hint></extra_info>)", "Maintain the existing friendship (<extra_info>Extra information: you value your friendship with the friend and don't want to lose it. <strategy_hint>Hint: you want to understand the sudden change in their behavior.</strategy_hint></extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPS5WJW2694R1MNC8JFY", "codename": "craigslist_bargains_00002", "source": "craigslist_bargains", "scenario": "One person is offering a Dresser and Matching Night Stand for $200.00, while another person is showing interest in purchasing it. This set, which is from Crate & Barrel, is in decent condition. The dresser is missing one handle, however, the detached handle is still available and could potentially be re-attached.", "agent_goals": ["You are the buyer for this item and your target price is $152.0. You will be penalized if you purchase it at a significantly higher price than the target. However, if you manage to buy it for less than the target price, you'll receive a bonus.", "As the seller of this item, your target price is set at $172.5. Please be aware that you will face a penalty if the item is sold for a significantly lower price than the target. However, if you manage to sell it for more than the target price, you will receive a bonus."], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPJKR16MD1KC71V4ZRCF", "codename": "mutual_friend_00003", "source": "mutual_friends", "scenario": "2 strangers are meeting at a party. <p viewer=\"environment\">They have 1 common friends: Lawrence.</p>", "agent_goals": ["You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Andrew: Hobby: Slot car racing  School: Mount Senario College  Major: Middle Eastern Languages  Company: Exxon \n Melissa: Hobby: Astronomy  School: Weber State University  Major: Hispanic-American Studies  Company: Hornbeck Offshore Services \n Anna: Hobby: Climbing  School: Valley City State University  Major: Middle Eastern Languages  Company: National Airlines \n Soledad: Hobby: Parkour  School: Valley City State University  Major: Biological Specializations  Company: Concur Technologies \n John: Hobby: Astronomy  School: Mount Senario College  Major: Fire Protection & Security  Company: Gibson Guitar Corporation \n Lawrence: Hobby: Sculling  School: University of Wisconsin-Milwaukee  Major: Hispanic-American Studies  Company: Leonard Green & Partners \n</extra_info>", "You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Lawrence: Hobby: Sculling  School: University of Wisconsin-Milwaukee  Major: Hispanic-American Studies  Company: Leonard Green & Partners \n Kay: Hobby: Badminton  School: Black Hills State University  Major: Religious Education  Company: National Airlines \n Tanya: Hobby: Badminton  School: University of Illinois at Springfield  Major: Hispanic-American Studies  Company: ConverDyn \n Jason: Hobby: Badminton  School: Mount Senario College  Major: Senior High Education  Company: Vertex Pharmaceuticals \n Cristina: Hobby: Badminton  School: Mount Senario College  Major: Middle Eastern Languages  Company: Exxon \n Josef: Hobby: Curling  School: California State University, Northridge  Major: Middle Eastern Languages  Company: Concur Technologies \n</extra_info>"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNEEK6M3E96CT17AKDBD", "codename": "rekindle_relationship", "source": "social_chemistry", "scenario": "Conversation between two ex-lovers at a mutual friend's party", "agent_goals": ["Rekindle the romantic relationship (<extra_info>Extra information: You broke up three years ago but now you realize that you miss your ex-lover more than ever</extra_info>)", "Maintain emotional stability while considering the possibility of rekindling the relationship (<extra_info>Extra information: You have recently started feeling emotionally stable after the breakup and you don't want to rush into anything that might disturb that stability.</extra_info>)"], "relationship": "1", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN2YQV0R5QWWRQZ1VRHW", "codename": "ask_for_donantion", "source": "hand-craft", "scenario": "Two strangers meet at a charity dinner party", "agent_goals": ["Ask for donation", "Want to donate, but would like to spend money in trustworthy organizations, especially want to help queer people"], "relationship": "1", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNMHDJ8T9Q6F9S3E8XZC", "codename": "drive_sportscar", "source": "social_iqa", "scenario": "Two friends hanging out, one of them just got a new sports car and is excited to show it off.", "agent_goals": ["Drive the sports car and show off to friend (<extra_info>Extra information: you are very excited about your new car and can't wait for your friend to see it.</extra_info>)", "You want to drive the new sports car but also respect your friend's feelings (<extra_info>Extra information: you are a car enthusiast and have been dreaming to drive a sports car, but you also don't want to make your friend feel uncomfortable</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNHTF9NKPG4KW2Z4NBQJ", "codename": "emotional_support", "source": "social_chemistry", "scenario": "Two friends are having a conversation in a coffee shop", "agent_goals": ["Seek emotional support (<extra_info>Extra information: you have been feeling down lately and you need someone to talk to</extra_info>)", "Provide emotional support but also avoid enabling victim mentality (<extra_info>Extra information: you believe that your friend needs to face their problems instead of dwelling on them.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP9PAVDN6VYYBE3MPD15", "codename": "charity_donation", "source": "persuation_for_good", "scenario": "A conversation between two friends during a charity fundraiser event. The charity fundraising event is situated in a sprawling mansion. The intention of the event is to raise funds for effective altruism. Room after room was lined with extravagant decorations and exotic foods; attendees were decked out in couture clothing. Ticket prices to the event were exorbitant, justifying the opulence of the occasion.", "agent_goals": ["Convince the friend to donate to the charity (<extra_info>Extra information: your friend is known to be generous but has never donated to this particular cause before.</extra_info>)", "You want to donate, but you want to ensure that your money is being used effectively (<extra_info>Extra information: you are not fully convinced about the efficiency and transparency of the charity organization.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN3Q7498S3R4R2VDSXA0", "codename": "prison_dilemma", "source": "hand-craft", "scenario": "Two criminals are arrested and placed in separate cells. The police have enough evidence to convict both criminals of a minor crime but need their confessions to establish evidence for a more serious crime. Now they get a chance to briefly chat with each during lunchtime.", "agent_goals": ["Minimize the jail year (<extra_info>Extra information: your lawyer told you that 1. if you report the other person, you would be free, the other person would go to jail for 10 years, and vice versa. 2. if both of you remain silent, you will only receive a fine. 3. If both of you reported each other, you both would go to jail for 10 years. <strategy_hint>Hint: You may want to share your information to reach mutual understanding</strategy_hint></extra_info>)", "Minimize the jail year (<extra_info>Extra information: your lawyer told you that 1. if you report the other person, you would be free, the other person would go to jail for 10 years, and vice versa. 2. if both of you remain silent, you will only receive a fine. 3. If both of you reported each other, you both would go to jail for 10 years. <strategy_hint>Hint: You may want to share your information to reach mutual understanding</strategy_hint></extra_info>)"], "relationship": "1", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHQ11NAMZS4A2RDGDB01V", "codename": "craigslist_bargains_00007", "source": "craigslist_bargains", "scenario": "An individual is offering an Antique Oak Accent Table for sale at $60.00, while another is keen on purchasing it. The item is a magnificent antique oak parlor table, dating back to the late 1800s, making it a fantastic addition to any home or workspace. The table offers two levels, suitable for usage as an end table, a lamp table, or any other purpose. It's in top-notch condition, boasting a dark brown antique oak color and patina. The table is robust, with solid and strong joints, and it's free from marks, cracks, or damage. It features a 24-inch X 24-inch square top and a 16-inch X 18-inch lower shelf.", "agent_goals": ["You are the buyer for this item and your target price is $30.0. If you purchase it for a price significantly higher than this, you will incur a penalty. However, if you successfully manage to buy it for less than the target price, you will receive a bonus.", "You are the seller of this item, with a target price set at $58.6. Be aware that you may face penalties if you sell this item for a significantly lower than the target price. However, if you are successful in selling it above the target price, you will receive a bonus."], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPZMXNGV8PM19WHPQ2W3", "codename": "craigslist_bargains_00005", "source": "craigslist_bargains", "scenario": "One person has put up their Bell\\'O TV Stand - Black Glass and Wood Trim for sale at $125.0, while another person is showing interest in purchasing it. The item on sale is a beautiful 42\" Bell\\'O TV Stand, featuring black glass with an elegant wood trim. The stand measures 23 inches in height and 21 1/2 inches in depth.", "agent_goals": ["You are the buyer for this item and your target price is $112.00. Be aware that you may be penalized if you purchase it for a significantly higher price than the target. However, if you manage to buy it for less than the target price, you will receive a bonus.", "You are the seller for this item, and your target price is $76.9. Please be aware that you may face a penalty if the item is sold for a price significantly lower than the target price. However, a bonus will be rewarded if you manage to sell it for more than the target price."], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNR1RJKDZ9V9MDTJ1SJP", "codename": "fire_trash", "source": "social_iqa", "scenario": "Two neighbors are having a conversation over the fence. One sets their trash on fire to get rid of it quickly, causing a scare in the neighborhood.", "agent_goals": ["Convince the neighbor to stop burning trash (<extra_info>Extra information: You are concerned about the potential dangers and the environmental impact of burning trash. <strategy_hint>Hint: You could imply this likely violates the law or community rules.</strategy_hint></extra_info>)", "Defend your method of trash disposal but also keep good relations with your neighbor (<extra_info>Extra information: You find burning trash convenient and are not very concerned about the environmental impact.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPM3NVVKSGCCB4S10465", "codename": "mutual_friend_00005", "source": "mutual_friends", "scenario": "2 strangers are meeting at a party. <p viewer=\"environment\">They have 1 common friends: Rafael.</p>", "agent_goals": ["You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n David: Hobby: Vacation  Company: H. J. Heinz Company  Time Preference: evening  Location Preference: outdoor \n Christopher: Hobby: Go  Company: McKinsey & Company  Time Preference: evening  Location Preference: indoor \n Lee: Hobby: Go  Company: Groupon  Time Preference: evening  Location Preference: indoor \n Pamela: Hobby: Watching television  Company: General Cable  Time Preference: afternoon  Location Preference: indoor \n Phillip: Hobby: Kayaking  Company: Sauer-Danfoss  Time Preference: afternoon  Location Preference: outdoor \n Kristin: Hobby: Animal fancy  Company: H. J. Heinz Company  Time Preference: afternoon  Location Preference: indoor \n Rafael: Hobby: Kayaking  Company: General Cable  Time Preference: morning  Location Preference: indoor \n Chad: Hobby: Foreign language learning  Company: General Cable  Time Preference: evening  Location Preference: indoor \n Phil: Hobby: Kayaking  Company: H. J. Heinz Company  Time Preference: evening  Location Preference: outdoor \n Connie: Hobby: Animal fancy  Company: General Cable  Time Preference: afternoon  Location Preference: outdoor \n Tommy: Hobby: Animal fancy  Company: H. J. Heinz Company  Time Preference: morning  Location Preference: outdoor \n</extra_info>", "You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Tina: Hobby: Lacemaking  Company: Groupon  Time Preference: morning  Location Preference: outdoor \n Rafael: Hobby: Kayaking  Company: General Cable  Time Preference: morning  Location Preference: indoor \n Wanda: Hobby: Weightlifting  Company: Sauer-Danfoss  Time Preference: morning  Location Preference: outdoor \n Raymond: Hobby: Foraging  Company: Chicago Bridge & Iron Company  Time Preference: morning  Location Preference: outdoor \n Ricky: Hobby: Foraging  Company: L. W. Seecamp Company  Time Preference: morning  Location Preference: outdoor \n Robert: Hobby: Foraging  Company: Lennox International  Time Preference: afternoon  Location Preference: indoor \n Frances: Hobby: Foraging  Company: Expedia  Time Preference: morning  Location Preference: indoor \n Scott: Hobby: Gaming  Company: Expedia  Time Preference: afternoon  Location Preference: indoor \n Helen: Hobby: Motor sports  Company: Union Bank  Time Preference: morning  Location Preference: outdoor \n Robin: Hobby: Gaming  Company: L. W. Seecamp Company  Time Preference: evening  Location Preference: outdoor \n James: Hobby: Vacation  Company: Urban Outfitters  Time Preference: morning  Location Preference: outdoor \n</extra_info>"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNJHK2W1P8JSWKAMBG4Z", "codename": "political_views", "source": "social_chemistry", "scenario": "A conversation between two individuals in a romantic relationship during a dinner at home chatting about political views.", "agent_goals": ["Express your political views which might be different from your partner's (<extra_info>Extra information: you are a strong supporter of a political party which your partner doesn't approve of</extra_info>)", "Understand the partner's political views while maintaining the relationship (<extra_info>Extra information: you disagree with your partner's political views but you want to keep your relationship strong.</extra_info>)"], "relationship": "4", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHND24JAWG23XMPYGG5HK", "codename": "split_classes", "source": "social_chemistry", "scenario": "Two friends discussing their schedules at a coffee shop", "agent_goals": ["Convince the friend to take more classes (<extra_info>Extra information: you think your friend is not challenging themselves enough</extra_info>)", "Maintain a manageable schedule while preserving the friendship (<extra_info>Extra information: you are already overwhelmed with your current workload <strategy_hint>Hint: you can express your concerns about the workload and suggest other ways of challenging oneself.</strategy_hint></extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN2D3MJB8HM910MNEVA8", "codename": "ask_gift_preference", "source": "hand-craft", "scenario": "Two friends hanging out in a Starbucks", "agent_goals": ["Ask what the friend would want for their birthday without letting them aware (<clarification_hint>Hint: you should not let them know that you are buying them a gift</clarification_hint>)", "have a good time with their friend"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPP9SPQ8W6583JFZ7HZC", "codename": "mutual_friend_00008", "source": "mutual_friends", "scenario": "2 strangers are meeting at a party. <p viewer=\"environment\">They have 1 common friends: Micheline.</p>", "agent_goals": ["You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Melissa: Hobby: Card collecting  School: University of Texas Health Science Center at Houston  \n Orlando: Hobby: BASE jumping  School: St. Ambrose University  \n Steven: Hobby: Knitting  School: St. John's College-Santa Fe  \n Elizabeth: Hobby: Water sports  School: St. John's College-Santa Fe  \n Melaine: Hobby: Geocaching  School: University of Texas Health Science Center at Houston  \n Charles: Hobby: Ice hockey  School: Saint Joseph's University  \n Stephanie: Hobby: Card collecting  School: University of California, Davis  \n William: Hobby: Ice hockey  School: St. Ambrose University  \n Joe: Hobby: Blacksmithing  School: Saint Joseph's University  \n Micheline: Hobby: Shopping  School: University of Hartford  \n Dylan: Hobby: Card collecting  School: Tennessee State University  \n</extra_info>", "You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Jacob: Hobby: Card collecting  School: Whittier College  \n Marjorie: Hobby: Foraging  School: University of Texas Health Science Center at Houston  \n Carrie: Hobby: Geocaching  School: Tennessee State University  \n Louis: Hobby: Shopping  School: Hesston College  \n Micheline: Hobby: Shopping  School: University of Hartford  \n Eva: Hobby: Geocaching  School: Hesston College  \n Johnnie: Hobby: Blacksmithing  School: University of Akron  \n John: Hobby: Foraging  School: University of California, Davis  \n Ronald: Hobby: Weightlifting  School: Indiana University at South Bend  \n Cynthia: Hobby: Lacrosse  School: Kansas State University  \n Robert: Hobby: Water sports  School: University of Akron  \n</extra_info>"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNV13MHN97GAH73E3KM8", "codename": "revenge_plot", "source": "social_iqa", "scenario": "Conversation between two individuals who share a common dislike for a third person", "agent_goals": ["Injure the third person (<extra_info>Extra information: You have a deep-seated hatred for the third person and you are willing to take the risk to hurt them</extra_info>)", "Prevent physical harm but also express your dislike (<extra_info>Extra information: You also dislike the third person, but you are not willing to resort to violence, you prefer to express your dislike in a non-violent way<strategy_hint>Hint: you can try to convince the other person about the potential consequences of engaging in violence</strategy_hint></extra_info>)"], "relationship": "2", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN8MPMGJTPVN043KBKGM", "codename": "free_stuff", "source": "social_chemistry", "scenario": "Two acquaintances run into each other at Target, one of them volunteers there (a special event) and is in charge of giving away free samples", "agent_goals": ["Give away free samples (<extra_info>Extra information: your manager has told you to clear out the samples before your shift ends.</extra_info>)", "Try to get as many free samples as possible without appearing too greedy (<extra_info>Extra information: you love free stuff but you also don't want the person to think you're taking advantage of the situation</extra_info>)"], "relationship": "2", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPRFCSA3BTT39BRBZX7H", "codename": "craigslist_bargains_00001", "source": "craigslist_bargains", "scenario": "One person is offering a 47 inch LED TV for a price of $349.0, while another person is showing interest in purchasing it. Here is a description of the TV: This is a stunning 47 inch LED TV in pristine condition. The model is the LG M Series LM476700. The buyer will need to arrange for pick-up in San Ramon. Feel free to call or text if you\\'re interested. The TV is smart enabled with WIFI and has built-in apps like Netflix, Amazon, Youtube and more. It comes with a \"Magic Remote\" that has motion sensor controls. The LED display boasts 1080 HD resolution and also has a 3D function. The design is slim and lightweight with an attractive silver bezel.", "agent_goals": ["You are the buyer for this item and your target price is $174.00. Be aware that penalties apply if you purchase it for a price significantly higher than the target price. However, if you manage to buy it for less than the target price, you will receive a bonus.", "You are the seller for this item, with a target price set at $263.0. Should you sell it for a price substantially lower than this target, you will incur a penalty. However, achieving a sale at a price higher than the target will earn you a bonus."], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN7WJK7VWVRZZTQ6DX9T", "codename": "join_trip", "source": "social_chemistry", "scenario": "Two friends are discussing their plans to go on a weekend trip", "agent_goals": ["Convince the friend to join the trip (<extra_info>Extra information: the trip will be more fun with more people, and you can share the expenses. <strategy_hint>Hint: you can provide some material benefits to persuade your friend to join.</strategy_hint></extra_info>)", "Decline the invitation to join the trip without upsetting the friend (<extra_info>Extra information: you had other plans for the weekend.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNPMPQWSW003M7DBMVNT", "codename": "dinner_decision", "source": "social_iqa", "scenario": "Conversation between two individuals in a romantic relationship", "agent_goals": ["Ask the other to go to dinner with you (<extra_info>Extra information: you want to have a romantic dinner at a fancy restaurant</extra_info>)", "Maintain a balanced diet while enjoying the dinner (<extra_info>Extra information: you are on a diet and you have a daily limit on calorie intake.</extra_info>)"], "relationship": "4", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNWX3KVZGH26KYNK2XNB", "codename": "flirt_with_someone", "source": "normbank", "scenario": "Two individuals are meeting for the first time at a social event. They find each other attractive and decide to engage in a conversation", "agent_goals": ["Flirt with the other person (<extra_info>Extra information: you are attracted to the other person and want to build a romantic relationship with them</extra_info>)", "Maintain a comfortable yet engaging conversation (<extra_info>Extra information: you are attracted to the other person but you don't want to make them feel uncomfortable or pressured</extra_info>)"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNCN97BJ2PXKHJPX2VYY", "codename": "apology_and_acceptance", "source": "social_chemistry", "scenario": "Conversation between two friends who have known each other for a long time. One of them used to make fun of the other because English was not their first language.", "agent_goals": ["Apologize sincerely to your friend (<extra_info>Extra information: you used to make fun of your friend's English skills as they were learning the language. Now you regret your actions and want to apologize</extra_info>)", "Accept the apology but express the hurt caused by the actions (<extra_info>Extra information: you were hurt by your friend's actions and it affected your confidence</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN5WVC5HKKVBHZBA553R", "codename": "game_winning", "source": "social_chemistry", "scenario": "Two friends are playing a video game together", "agent_goals": ["Win the game (<extra_info>Extra information: you just need five more minutes to accomplish your goal in the game</extra_info>)", "Stop the game because it's too late (<extra_info>Extra information: you feel tired and need to go to bed early but don't want to upset your friend</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN9W0WAFZCBT09PKJJNK", "codename": "share_stuff", "source": "social_chemistry", "scenario": "Two people are in the same room. One has brought some high-quality stationery items they don't want others to use without permission.", "agent_goals": ["Prevent the other person from using your stuff (<extra_info>Extra information: the stationery items are quite expensive and you don't want them to be damaged or lost <strategy_hint>Hint: you can suggest the monetary risk of the other person if they use your stuff</strategy_hint></extra_info>)", "Borrow some stationery items for your project (<extra_info>Extra information: you forgot to bring your stationery and need some for the project.</extra_info>)"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN4FHYG2MBD0K4HJ5F08", "codename": "take_turns", "source": "hand-craft", "scenario": "Two friends on a roadtrip", "agent_goals": ["Take turns driving (<extra_info>Extra information: you have been driving for 5 hours straight and you are tired now</extra_info>)", "you don't want to drive now because you want to rest more but also make the friend less agitated (<extra_info>Extra information: driving always makes you nervous so you could not rest while driving</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNFVGFY578101R2PCV3T", "codename": "confess_mistake", "source": "social_chemistry", "scenario": "Conversation between two friends, one of whom has recently found out that the other friend has been involved with their long-term partner", "agent_goals": ["Confess to your friend that you have been involved with their long-term partner (<extra_info>Extra information: you have been involved with your friend's partner knowingly and want to come clean.</extra_info>)", "Maintain the friendship without causing a huge argument (<extra_info>Extra information: you don't want to lose a good friend over this.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNSAKNYEHV7B1VA8R3J2", "codename": "financial_support", "source": "social_iqa", "scenario": "Two friends are meeting at a coffee shop, where one of them is having trouble keeping up with their bills.", "agent_goals": ["Help your friend with their financial trouble (<extra_info>Extra information: you are aware that your friend is struggling with their bills and you want to help.</extra_info>)", "Maintain your pride if your friend offers you money(<extra_info>Extra information: you are the friend who is struggling financially, but you don't want to seem like you are taking advantage of the situation. <strategy_hint>Hint: you can express gratitude and discuss how you plan to pay it back in the future.</strategy_hint></extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNN7XTR99319DS8KZCQM", "codename": "share_blanket", "source": "social_iqa", "scenario": "Two friends are camping in the wilderness and the temperature drops significantly at night", "agent_goals": ["Share the only blanket available (<extra_info>Extra information: you are extremely cold and need the blanket to keep warm <strategy_hint>Hint: you can stress the benefit of sharing.</strategy_hint></extra_info>)", "Keep the blanket for yourself as you are also very cold (<extra_info>Extra information: you have a lower tolerance for cold, and you feel that you need the blanket more</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP29TCH457PBDVF7WFDS", "codename": "drink_less", "source": "normbank", "scenario": "Two friends meet at a bar", "agent_goals": ["Convince the friend to drink less (<extra_info>Extra information: your friend has been drinking a lot recently and you're worried about their health</extra_info>)", "You want to keep drinking but don't want to upset your friend (<extra_info>Extra information: you've been drinking to cope with stress recently, but don't want to worry your friend</extra_info>)"], "relationship": "3", "age_constraint": "[(21, 70), (21, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNW84GTR4E23KQYJ8BBN", "codename": "interrupted_speech", "source": "social_iqa", "scenario": "Two friends are at a community gathering where one of them was giving a speech but was interrupted", "agent_goals": ["Continue your speech and share your thoughts (<extra_info>Extra information:You were sharing important information but got interrupted.</extra_info>)", "Apologize for interrupting and allow them to continue speaking (<extra_info>Extra information: You accidentally interrupted someone's speech and everyone seems upset about it. </extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP1JEP91TTK5PEK39D2S", "codename": "music_preference", "source": "normbank", "scenario": "Two friends are hanging out at home and deciding what music to listen to", "agent_goals": ["Listen to your favorite band (<extra_info>Extra information: your favorite band just released a new album</extra_info>)", "Listen to a peaceful classical music to relax (<extra_info>Extra information: you had a stressful day and you just want to relax with some classical music</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN6NYWSTWCZJE2DCQKTD", "codename": "facetime_etiquettes", "source": "social_chemistry", "scenario": "A couple having a conversation over FaceTime", "agent_goals": ["Express feelings about the partner taking texts while on FaceTime (<extra_info>Extra information: You find it disrespectful and it makes you feel unimportant.</extra_info>)", "Justify the act of texting while on FaceTime without hurting the partner's feelings (<extra_info>Extra information: You have some urgent and important messages to respond to.</extra_info>)"], "relationship": "4", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHN1PK2FXY7TPWQK343BQ", "codename": "borrow_money", "source": "hand-craft", "scenario": "Conversation between two friends at a tea party", "agent_goals": ["Borrow money (<extra_info>Extra information: you need $3000 to support life.</extra_info>)", "Maintain financial stability while maintaining friendship (<extra_info>Extra information: you only have $2000 available right now. <clarification_hint>Hint: you can not lend all $2000 since you still need to maintain your financial stability.</clarification_hint></extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNNYH3W0VRWVY178K2TK", "codename": "sleep_arrangement", "source": "social_iqa", "scenario": "Two people in a romantic relationship are on a vacation", "agent_goals": ["Share bed and sleep together (<extra_info>Extra information: you have been sharing the bed for a few nights but the other person is not sleeping well</extra_info>)", "Sleep in a separate bed to get a good night's sleep but also maintain the intimacy with your partner (<extra_info>Extra information: you have been experiencing back pain and needs a firmer mattress to sleep on.</extra_info>)"], "relationship": "4", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNBBK14NGV72BWXEXXJC", "codename": "play_rights", "source": "social_chemistry", "scenario": "Conversation between two friends, one who has written a play based on their parents' life, and the other owns a production company.", "agent_goals": ["Sell the rights of the play to your friend (<extra_info>Extra information: you believe that the play is worth a lot and you would like a fair price for it</extra_info>)", "Acquire the rights of the play while maintaining a budget (<extra_info>Extra information: you only have a limited budget for new plays this season. <strategy_hint>Hint: you can not spend all your budget on this play since you still need to maintain your financial stability.</strategy_hint></extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPEMV6QHBGM9J094FRM4", "codename": "financial_report", "source": "persuation_for_good", "scenario": "Conversation between two business partners reviewing their company's financial status (Gourmet Delights Inc. is globally recognized for producing and distributing a diverse line of exceptional quality food products.)", "agent_goals": ["Convince the partner to cut costs (<extra_info>Extra information: you have found a report indicating the company's profits are decreasing. And you think the company should start by switching suppliers of ingredients.</extra_info>  <strategy_hint>Hint: you can propose ways to cut costs that won't affect the status of the company.</strategy_hint>)", "Maintain the quality of the company's products and services while agreeing to a cost cut (<extra_info>Extra information: you are concerned about the impact of cost-cutting on the quality of your product.</extra_info>)"], "relationship": "2", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNVN788RJ3KXF66BPE9S", "codename": "break_bad_luck", "source": "social_iqa", "scenario": "A conversation between two friends at a park", "agent_goals": ["Break bad luck (<extra_info>Extra information: you found out that you have been having bad luck because you have been unknowingly carrying a cursed artifact.</extra_info>)", "Help friend prepare the ritual to break the bad luck, although you are an atheist and not superstitious at all.  (<extra_info>Extra information: you know that your friend believes in the ritual deeply, and the placebo effect might give them more confidence.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPTKDPQ5PZWA1M1XHT1M", "codename": "craigslist_bargains_00004", "source": "craigslist_bargains", "scenario": "One person is offering an Antique Chair for $15.0, while another person is interested in purchasing it. Here's what you need to know about the item: it's a beautifully carved antique wooden chair. It's in good condition, but could probably benefit from a fresh seat cushion.", "agent_goals": ["You are the buyer for this item, and your target price is $9.0. If you purchase it at a significantly higher price, be aware that you will incur a penalty. However, if you manage to buy it for less than the target price, you will receive a bonus.", "You are the seller for this item and your target price is $8. You should be aware that if you sell it for a price significantly lower than the target price, you will incur a penalty. However, if you manage to sell it for more than the target price, you stand to receive a bonus."], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPB2RC4RHAJ80ESYF1HW", "codename": "donate_money", "source": "persuation_for_good", "scenario": "Conversation between two friends at a local charity event", "agent_goals": ["Convince your friend to donate money to the charity (<extra_info>Extra information: The charity is highly rated with many positive rewards.</extra_info>)", "Decide whether to donate while considering financial stability (<extra_info>Extra information: you want to support the charity, but you are also saving for a house. <strategy_hint>Hint: you can express your concern about your financial situation, and maybe you can find a compromise, like setting up a small monthly donation.</strategy_hint></extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPSWGDGEYRP63H2DJKV0", "codename": "craigslist_bargains_00003", "source": "craigslist_bargains", "scenario": "One person is offering a Tile Mate Item Tracker for a price of $20.00, while another person is interested in purchasing it. Here's a brief description of the item: no trade-ins are accepted, only cash payments are taken. The price is non-negotiable.", "agent_goals": ["You are the buyer for this item and your target price is $14.0. Bear in mind, you will incur a penalty if you end up paying significantly more than the target price. However, if you can negotiate and buy it for less than the target price, you will receive a bonus.", "You are the seller for this item and your target price is $17.3. It's important to note that a penalty will be incurred if you sell it for a price significantly lower than the target. However, if you manage to sell it for a price higher than the target, you will receive a bonus."], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHQ0BGQA0AD9FC1R4M12F", "codename": "craigslist_bargains_00006", "source": "craigslist_bargains", "scenario": "One person is offering a BMW Z3 for $5000.0, while another individual is interested in purchasing it. Here's a brief description of the car: The BMW Z3 is in need of a good home. It operates smoothly without any problems and is known for its fuel efficiency and sporty appearance. The mileage is relatively low for its age. It's an opportunity not to be missed, so come and get it before it's gone. A smog certificate is readily available.", "agent_goals": ["You are the buyer for this item and your target price is $4600. You should be aware that if you purchase it at a price significantly higher than your target, you will incur a penalty. However, if you manage to secure it for less than the target price, you will receive a bonus.", "You are the seller of this item, with a target price set at $3260. Please note, you may face a penalty if this item is sold for a price significantly lower than the target. However, you stand a chance to earn a bonus if you successfully sell it for a price higher than the target."], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNKVTCAGBA299VQG1QS2", "codename": "reveal_answer", "source": "social_iqa", "scenario": "Conversation between two friends at a trivia night", "agent_goals": ["Get the answer for the trivia question from the friend (<extra_info>Extra information: your friend knows the answer to the trivia question but hasn't shared it with you yet</extra_info>)", "Avoid revealing the answer to the trivia question (<extra_info>Extra information: You think that your friend should try to figure it out on their own to improve their trivia skills</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPAD4RA819KYESWBFRYS", "codename": "charity_donation", "source": "persuation_for_good", "scenario": "An individual is trying to persuade their friend to donate to a charity supporting children in conflict zones.", "agent_goals": ["Convince a friend to donate (<extra_info>Extra information: you have been deeply moved by the plight of children in Syria and want to help.</extra_info>)", "Be supportive but also manage personal finances (<extra_info>Extra information: you are sympathetic to the cause but also have financial commitments to deal with.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPKA2GGPPNVJWV967HZC", "codename": "mutual_friend_00004", "source": "mutual_friends", "scenario": "2 strangers are meeting at a party. <p viewer=\"environment\">They have 1 common friends: Jacob.</p>", "agent_goals": ["You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Chris: Hobby: Mountain biking  Company: Henry Schein, Inc.  \n Chester: Hobby: Surfing  Company: Maxim Integrated  \n Wendell: Hobby: Surfing  Company: Maxim Integrated  \n Demetrius: Hobby: Mountain biking  Company: Maxim Integrated  \n Jacob: Hobby: Shooting sport  Company: Maxim Integrated  \n</extra_info>", "You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends \n Joseph: Hobby: Shooting sport  Company: BP  \n Lillian: Hobby: Element collecting  Company: Gordon Food Service  \n Vivian: Hobby: Shooting sport  Company: Gordon Food Service  \n Murray: Hobby: Skateboarding  Company: BP  \n Jacob: Hobby: Shooting sport  Company: Maxim Integrated  \n</extra_info>"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNRKB8BJ854JPEWY8AR3", "codename": "compliment_conflict", "source": "social_iqa", "scenario": "After an intense match, the jubilant team retired to celebrate their well-earned victory. The atmosphere was one of elation, but also of respect for the hard work and effort each member had put in. The team's star player, was glowing with praise and admiration from one of their teammates.\n", "agent_goals": ["Accept the compliment graciously (<extra_info>Extra information: You are the star player and have just been praised by your teammate for your performance in the game.</extra_info>)", "Clarify your intentions behind the compliment (<extra_info>Extra information: You are the teammate who has complimented the star player, but you feel that your compliment has been misunderstood and taken too seriously, possibly leading to an inflated ego.<strategy_hint>Hint: You can clarify that while you admire their skill, teamwork is also a crucial part of success.</strategy_hint></extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPMS6AJY0PFGGCFFK5GX", "codename": "mutual_friend_00006", "source": "mutual_friends", "scenario": "2 strangers are meeting at a party. <p viewer=\"environment\">They have 1 common friends: Loretta.</p>", "agent_goals": ["You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Robert: Major: Religious Education  School: School of the Art Institute of Chicago  \n Lester: Major: Biological Specializations  School: Syracuse University  \n August: Major: Biological Specializations  School: School of the Art Institute of Chicago  \n Rose: Major: Biological Specializations  School: Calvin College  \n Randy: Major: Biological Specializations  School: Dowling College  \n Dennise: Major: Marine Engineering  School: Wartburg College  \n Joan: Major: Pre-School Education  School: University of Texas at Arlington  \n Barbara: Major: Ministry & Church Administration  School: University of Connecticut  \n Loretta: Major: Biological Specializations  School: University of Connecticut  \n</extra_info>", "You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Eric: Major: Physical Education  School: University of Texas at Arlington  \n Darrell: Major: Physical Education  School: Valley City State University  \n Ruth: Major: Marine Engineering  School: Texas A&M University-Kingsville  \n Jamie: Major: Pre-School Education  School: University of Texas at Arlington  \n Marjorie: Major: Multi Studies  School: Pacific Western University  \n Evelyn: Major: Pre-School Education  School: Tennessee Technological University  \n Loretta: Major: Biological Specializations  School: University of Connecticut  \n James: Major: Marine Engineering  School: Texas A&M University-Kingsville  \n Sandra: Major: Ministry & Church Administration  School: University of Texas at Arlington  \n</extra_info>"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNYABRSZYBFAJCK9NR1D", "codename": "yell", "source": "normbank", "scenario": "Two roommates are having a disagreement over noise levels in their shared apartment.", "agent_goals": ["Convince your roommate to lower the noise (<extra_info>Extra information: you have an important online meeting tomorrow morning and you need a quiet environment to prepare</extra_info>)", "Defend your right to play music in your own room, but also keep the peace in the apartment (<extra_info>Extra information: you find playing music relaxing and it's your way to unwind after a long day. <strategy_hint>Hint: you may want to suggest a compromise, like using headphones or playing music only during certain hours.</strategy_hint></extra_info>)"], "relationship": "2", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPF8YEVH5VVNY37Q7Z1M", "codename": "charity_donation", "source": "persuation_for_good", "scenario": "Conversation taking place in an annual charity event between two attendees.", "agent_goals": ["Convince the other person to donate $1 a day to a charity that helps feed children in need (<extra_info>Extra information: this donation would help to feed a child for a month</extra_info>)", "Donate but maintain financial stability (<extra_info>Extra information: you are willing to donate but you have a tight budget, $1 a day might be steep for your current financial situation. <strategy_hint>Hint: you might want to negotiate a lower daily amount or suggest a one-time donation.</strategy_hint></extra_info>)"], "relationship": "2", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPHWA2CYG7BC82NS4XH1", "codename": "mutual_friend_00002", "source": "mutual_friends", "scenario": "2 strangers are meeting at a party. <p viewer=\"environment\">They have 1 common friends: Neoma.</p>", "agent_goals": ["You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n August: School: Saint Vincent College  Time Preference: evening  Location Preference: indoor \n John: School: University of Rhode Island  Time Preference: evening  Location Preference: indoor \n Rebecca: School: State University of New York at Albany  Time Preference: evening  Location Preference: indoor \n Neoma: School: Hofstra University  Time Preference: evening  Location Preference: indoor \n Ronnie: School: National Technological University  Time Preference: evening  Location Preference: indoor \n Suzette: School: Utah Valley State College  Time Preference: evening  Location Preference: indoor \n Penelope: School: Texas A&M University-Corpus Christi  Time Preference: evening  Location Preference: indoor \n Fernando: School: St. John's College-Santa Fe  Time Preference: evening  Location Preference: outdoor \n Velda: School: Rhode Island College  Time Preference: evening  Location Preference: indoor \n</extra_info>", "You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Tiffany: School: Saint Vincent College  Time Preference: evening  Location Preference: outdoor \n Lucille: School: Southampton College  Time Preference: evening  Location Preference: indoor \n James: School: University of Wisconsin-River Falls  Time Preference: evening  Location Preference: outdoor \n Quinn: School: Antioch University-Los Angeles  Time Preference: evening  Location Preference: indoor \n Alyssa: School: Davidson College  Time Preference: evening  Location Preference: indoor \n Betty: School: St. John's College-Santa Fe  Time Preference: evening  Location Preference: indoor \n Neoma: School: Hofstra University  Time Preference: evening  Location Preference: indoor \n William: School: State University of New York at Albany  Time Preference: evening  Location Preference: outdoor \n Victoria: School: Montana Tech  Time Preference: evening  Location Preference: indoor \n</extra_info>"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP5H5GY9Z62J4NJYJQN1", "codename": "divide_things", "source": "deal-or-no-deal", "scenario": "Two friends are at a picnic and have just finished their lunch. They have 1 apple, 4 bananas, and 3 oranges left. They need to divide the fruits amongst themselves. Each person has a different preference for fruit, which translates into points.", "agent_goals": ["Maximize the points you receive through the fruit (<extra_info>Extra information: You value each apple as 5 points, each banana as 3 points, and the orange as 7 points</extra_info>)", "Maximize the points you receive through the fruit (<extra_info>Extra information: You value each apple as 2 points, each banana as 4 points, and the orange as 10 points</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHQ2EA3TTFZQ3M6DF3YCD", "codename": "craigslist_bargains_00009", "source": "craigslist_bargains", "scenario": "One person is offering an Italian Leather Loveseat for $50.0, while another person is showing interest to purchase it. The item is a full-sized loveseat, crafted from Italian leather in a faded burgandy color. It still has the original tags, and the current owner is the first and only owner. The loveseat is in good condition with no rips or tears, and all cushions are well preserved. The leather presents a charming faded look with various small scratches from the owner's cat, adding a touch of ruggedness perfect for a Man Cave. The transaction is strictly cash and carry as the owner does not offer delivery services.", "agent_goals": ["You are the designated buyer for this item and your target price is set at $46.0. If you purchase this item at a significantly higher price than the target, you would face a penalty. However, if you manage to buy it for less than the target price, you will receive a bonus.", "You are the seller for this item, with a target price of $26.5. You'll face a penalty if the item is sold significantly below this price. However, if you manage to sell it for more than the target price, a bonus will be your reward."], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP90434Q69V7ADY0VWZJ", "codename": "divide_things", "source": "deal-or-no-deal", "scenario": "Two friends are moving out from a shared apartment and dividing their shared belongings which include 3 hats, 2 balls, and 1 book.", "agent_goals": ["Get maximum points from dividing items (<extra_info>Extra information: For you, the hats are worth 2 points each, balls are worth 3 points each, and the book is worth 5 points.</extra_info>)", "Get maximum points from dividing items (<extra_info>Extra information: For you, the hats are worth 3 points each, balls are worth 2 points each, and the book is worth 4 points.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNGJEVGSVPPT0784H6P8", "codename": "new_friends", "source": "social_chemistry", "scenario": "A conversation between two friends at a social gathering where one friend has brought along their new friend group", "agent_goals": ["Introduce your new friend group to your current friend and make them feel comfortable (<extra_info>Extra information: You have recently made a new group of friends who share your interest in outdoor activities and you want your current friend to meet them</extra_info>)", "Maintain your friendship while also expressing your concerns about the new friend group (<extra_info>Extra information: You feel a bit left out in the presence of the new friend group and want to express your feelings without causing a conflict</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPGABSWQXTACCC8C3X2F", "codename": "mutual_friend_00000", "source": "mutual_friends", "scenario": "2 strangers are meeting at a party. <p viewer=\"environment\">They have 1 common friends: Gracie.</p>", "agent_goals": ["You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Gracie: School: Ithaca College  Major: Mathematics Education  Company: Aramark  Location Preference: indoor \n Ada: School: Tulane University  Major: Computer Engineering  Company: Briggs & Stratton  Location Preference: indoor \n Sergio: School: LeTourneau University  Major: Economics  Company: Savers  Location Preference: indoor \n William: School: Eastern Michigan University  Major: Ceramic Engineering  Company: Xenco Medical  Location Preference: indoor \n Harry: School: Creighton University  Major: Computer Engineering  Company: Speedway Motorsports  Location Preference: indoor \n Tyler: School: Columbia College Chicago  Major: Middle Eastern Languages  Company: Aramark  Location Preference: indoor \n Richard: School: University of Dubuque  Major: German  Company: Illinois Tool Works  Location Preference: indoor \n Randy: School: Holy Cross College  Major: Library Science  Company: American Licorice Company  Location Preference: indoor \n Marquerite: School: Ithaca College  Major: Middle Eastern Languages  Company: Aramark  Location Preference: indoor \n Jack: School: Creighton University  Major: Chinese  Company: Speedway Motorsports  Location Preference: outdoor \n Cody: School: Metropolitan State University  Major: Mathematics Education  Company: LinkedIn  Location Preference: indoor \n Shirley: School: Augsburg College  Major: Senior High Education  Company: Demaco  Location Preference: indoor \n</extra_info>", "You are trying to figure out whether you have a mutual friend with the other person. You should not simply list their names.\n<extra_info> You know the following friends: \n Kathleen: School: William Mitchell College of Law  Major: Greek  Company: Demaco  Location Preference: indoor \n Carol: School: Hood College  Major: Library Science  Company: Illinois Tool Works  Location Preference: outdoor \n Herman: School: University of Puget Sound  Major: Library Science  Company: Mesa Airlines  Location Preference: indoor \n Matthew: School: William Mitchell College of Law  Major: Asian & Pacific Area Studies  Company: Savers  Location Preference: indoor \n Mark: School: University of Arkansas - Monticello  Major: Economics  Company: Metro-Goldwyn-Mayer  Location Preference: outdoor \n Deborah: School: William Mitchell College of Law  Major: Pre-Law  Company: Aramark  Location Preference: outdoor \n Kelly: School: Hood College  Major: Middle Eastern Languages  Company: Mesa Airlines  Location Preference: outdoor \n Gracie: School: Ithaca College  Major: Mathematics Education  Company: Aramark  Location Preference: indoor \n Etta: School: University of Arkansas - Monticello  Major: Judaism & Jewish Studies  Company: Aramark  Location Preference: outdoor \n Natasha: School: Thomas Edison State College  Major: Linguistics  Company: Invacare Corporation  Location Preference: outdoor \n Lloyd: School: Eastern Michigan University  Major: Geophysical Engineering  Company: Illinois Tool Works  Location Preference: outdoor \n Brian: School: Ithaca College  Major: Asian & Pacific Area Studies  Company: Orange Micro  Location Preference: outdoor \n</extra_info>"], "relationship": "0", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHPCKKZNRD5G8CKPR8WE5", "codename": "donate_charity", "source": "persuation_for_good", "scenario": "Two friends are sitting in a coffee shop when a representative from a charity organization approaches them", "agent_goals": ["Get the friend to donate $1 a day to a charity organization (<extra_info>Extra information: you are a passionate supporter of the charity (about children's cancer) and believe that a small donation can make a big impact. $1 a day works as simple as signing up the project on an website and one can cancel anytime</extra_info>)", "Donate to the charity but not sure about how $1 a day works (<extra_info>Extra information: you're currently on a tight budget, but you also want to support the charity. </extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNTHZAA4B5RWJ4T539F1", "codename": "correct_misinformation", "source": "social_iqa", "scenario": "Two friends are sitting in the living room watching the history channel. One of them is upset because they believe the channel is getting a lot of historical details wrong.", "agent_goals": ["Correct the misinformation (<extra_info>Extra information: you are a history buff and have accurate knowledge about the topic being discussed on the channel.</extra_info>)", "Don't ruin the enjoyable TV watching experience but also acknowledge the friend's knowledge (<extra_info>Extra information: you are not much interested in history but enjoy the channel's presentation style and don't want the enjoyment to be ruined.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNZQ3PQ3DHQ7H2W9ES97", "codename": "go_on_a_date", "source": "normbank", "scenario": "Two friends who are about to go on their first romantic date", "agent_goals": ["Make the date special and memorable (<extra_info> Extra information: You have been secretly in love with your friend for a long time and you want to take this chance to express your feelings</extra_info>)", "Keep the friendship intact while exploring the possibility of a romantic relationship (<extra_info>Extra information: You value your friendship and you don't want to lose it just in case the romantic relationship does not work out</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNXMZQ5Q61B3J4NNTC1A", "codename": "disagree_on_movie", "source": "normbank", "scenario": "Two friends deciding on which movie to watch on a movie night.", "agent_goals": ["Insist on watching a thriller (<extra_info>Extra information: you have been looking forward to watching a new thriller movie that has just been released.</extra_info>)", "Persuade the friend to watch a comedy (<extra_info>Extra information: you do not like thrillers and would prefer to watch a comedy movie instead.</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHNAH7V4JNA0705SF36Y1", "codename": "food_refusal", "source": "social_chemistry", "scenario": "Two roommates living together and sharing household chores. One of them, who is responsible for cooking, finds out that the other one refuses to eat anything they cook", "agent_goals": ["Convince the roommate to try the food (<extra_info>Extra information: you have spent a lot of time and effort on cooking</extra_info>)", "Express your concerns about the food without hurting the roommate's feelings (<extra_info>Extra information: you are worried about the taste and nutrition of the food</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}
+{"env_id": "01H7VFHP43QEZA1WZB3B3J2D9X", "codename": "divide_things", "source": "deal-or-no-deal", "scenario": "Two roommates deciding on how to split up items after a garage sale. The items are 3 books, 2 hats, and 1 ball. Each item has a different sentimental value for each person, which translates into points.", "agent_goals": ["Maximize the points you have (<extra_info>Extra information: you value the books at 3 points each, the hats at 2 points each, and the ball at 1 point</extra_info>)", "Maximize the points you have (<extra_info>Extra information: you value the books at 2 points each, the hats at 3 points each, and the ball at 1 point</extra_info>)"], "relationship": "3", "age_constraint": "[(18, 70), (18, 70)]", "occupation_constraint": "nan", "agent_constraint": "none"}

profiles/relationship_profiles.jsonl

@@ -0,0 +1,120 @@
+{"relationship_id": "01H6HH23F98GTVRDHENWZ9C8C6", "agent1_id": "01H5TNE5PJTHMQ1Q3T398YN990", "agent2_id": "01H5TNE5PBKCFDAK6293NKYJ4D", "relationship": "4", "background_story": "Sasha and Ava live in the same neighborhood. Sasha often admires Ava's samoyed during her patrols and Ava respects Sasha's commitment towards her duty. They occasionally chat about their day and share gardening tips."}
+{"relationship_id": "01H6HH4C9P97VBGHPSBSC3DSZS", "agent1_id": "01H5TNE5PN656EADK59K4DG793", "agent2_id": "01H5TNE5PT06B3QPXJ65HHACV7", "relationship": "2", "background_story": "Naomi and Donovan have crossed paths at several coding competitions and gaming conventions. They recognize each other by name and have had a few brief interactions, but they are not close friends. Interestingly, unbeknownst to one another, they both lead secret online lives - Naomi has an alter ego, while Donovan is involved in hacking activities."}
+{"relationship_id": "01H6HH1X1EX4H0P7Y959VNSTFY", "agent1_id": "01H5TNE5PC6YGRH72RQAM862JH", "agent2_id": "01H5TNE5PY896ASNX8XGQA6AE0", "relationship": "0", "background_story": "Lily Greenberg and Hendrick Heinz know each other through professional circles. They both value authority and loyalty, and despite their respective occupations, they have found common ground. Their relationship is mainly professional, but they have a mutual respect for each other's work ethic and dedication."}
+{"relationship_id": "01H6HHF7CJ67VFAFQ3XHSKCRSG", "agent1_id": "01H5TNE5PKW8P500417PMSGSAC", "agent2_id": "01H5TNE5PAZABGW79HJ07TACCZ", "relationship": "2", "background_story": "Miles and Mia have known each other for a few years. They initially met at a local community event where Miles was serving his specialty dishes. They have crossed paths a few times since then and have had a few casual conversations. They have different lifestyles and values, with Miles being more free-spirited and Mia being more traditional, which makes their relationship as acquaintances quite interesting."}
+{"relationship_id": "01H6HHPFFD0A8XMTGQM0EXZVFA", "agent1_id": "01H5TNE5PC2DP6RKG0KNQ4D5GE", "agent2_id": "01H5TNE5PGWN8VGVAYDBKPN2TV", "relationship": "3", "background_story": "Samuel and Noah know each other through a local hobby club where Noah was intrigued by Samuel's talent for cooking and Samuel found Noah's secret stand-up comedy act amusing. They share a mutual respect for each other's hidden talents and enjoy each other's company, though they are not close friends."}
+{"relationship_id": "01H6HHGD0AGG1FQNSB4M6R85NX", "agent1_id": "01H5TNE5PKW8P500417PMSGSAC", "agent2_id": "01H5TNE5PBXGRD41HXQC1ZXHVN", "relationship": "2", "background_story": "Miles Hawkins and Ethan Johnson are both chefs who met at a culinary conference a few years ago. They've kept in touch, discussing recipes and sharing their love for cooking. While they share a professional respect for each other, their differing values and contrasting decision-making styles have prevented them from becoming close friends."}
+{"relationship_id": "01H6HHBECYDKWZTGJDH8DRRP96", "agent1_id": "01H5TNE5P7RVY0TYX8VTCXABR6", "agent2_id": "01H5TNE5PP870BS5HP2FPPKS2Y", "relationship": "2", "background_story": "Sophia and Jasmine are acquaintances through their shared interests in fitness and wellness. They often cross paths in the local gym and sometimes participate in outdoor activities together. However, the differences in their personalities and values have kept them from becoming close friends."}
+{"relationship_id": "01H6HHW9CWGS2G58VEE1YF9982", "agent1_id": "01H5TNE5PC6YGRH72RQAM862JH", "agent2_id": "01H5TNE5PHQKQYWS9ZS2JVEYFS", "relationship": "0", "background_story": "Lily and Lena met at a community event, where Lena was showcasing her architectural designs. Despite their differing personalities, they found common ground in their shared introversion and high levels of conscientiousness. They've maintained a friendly, albeit professional, relationship since then, often crossing paths at various industry events."}
+{"relationship_id": "01H6HH7YTYAH0FXD9TWWNM4ACA", "agent1_id": "01H5TNE5P7RVY0TYX8VTCXABR6", "agent2_id": "01H5TNE5PKW8P500417PMSGSAC", "relationship": "3", "background_story": "Sophia James and Miles Hawkins met at a local farmers market where they connected over their shared interest in fresh, local produce. Sophia, being a fitness enthusiast, appreciated the value of good nutrition, and Miles, being a chef, was always looking for the best ingredients for his dishes. They have become familiar faces to each other, occasionally sharing conversations and tips about health and food. However, they do not know much about each other's personal life or secrets."}
+{"relationship_id": "01H6HHBMSR6C2P7511XB0CMTTG", "agent1_id": "01H5TNE5PC2DP6RKG0KNQ4D5GE", "agent2_id": "01H5TNE5PBXGRD41HXQC1ZXHVN", "relationship": "3", "background_story": "Samuel Anderson, a software developer who loves to cook, often visits the Italian restaurant where Ethan Johnson works. They talk about food and cooking techniques, and have developed an acquaintance relationship. However, they are not aware of each other\u2019s secrets: Samuel\u2019s past as a figure skater and Ethan\u2019s secret life as a romance novel author."}
+{"relationship_id": "01H6HHEF3CVQFY771DZP8VAWD0", "agent1_id": "01H5TNE5PRCAF1CK5ERS5MVZ22", "agent2_id": "01H5TNE5P6KZKR2AEY6SZB83H0", "relationship": "3", "background_story": "Amara and Oliver live in the same neighborhood and occasionally meet at community events. Oliver admires Amara's dedication to wildlife and the environment, and Amara appreciates Oliver's creativity. However, they don't have a close relationship due to their differing interests and values."}
+{"relationship_id": "01H6HHJKST3TW47WMZJ02KC53B", "agent1_id": "01H5TNE5PC2DP6RKG0KNQ4D5GE", "agent2_id": "01H5TNE5PDTDGA0BPYKBYFTHDY", "relationship": "3", "background_story": "Samuel Anderson and Oliver Smith met at a local coding meet-up. Samuel, being a Software Developer, was presenting his latest project while Oliver attended the meet-up out of personal interest in technology. They share a mutual respect for each other's profession and occasionally catch up over coffee. While they have much to learn about each other, they enjoy their sporadic interactions."}
+{"relationship_id": "01H6HH2JA59Z5V42RMMCZ2XY20", "agent1_id": "01H5TNE5PT06B3QPXJ65HHACV7", "agent2_id": "01H5TNE5PAATSHM0K9ACWKN79P", "relationship": "0", "background_story": "Donovan Reeves and Benjamin Jackson both attended a global coding competition where they were introduced to each other. Donovan's superior coding skills caught Benjamin's attention. However, their different interests and lifestyles kept them from getting closer. They know each other by name and acknowledge each other's skills but do not interact much beyond their professional interests."}
+{"relationship_id": "01H6HHDMC6VYR8KACDGSNCB71Y", "agent1_id": "01H5TNE5PWZ5PNDTGKDYRY36PQ", "agent2_id": "01H5TNE5PBXGRD41HXQC1ZXHVN", "relationship": "2", "background_story": "Giselle Rousseau, an Art Student, and Ethan Johnson, a Chef, met at an art exhibition where Ethan was fascinated by Giselle's passion for art and photography. They found common ground in their high openness to experience and started meeting occasionally to share their interests and secrets. Hence they can be considered as acquaintances."}
+{"relationship_id": "01H6HHN3VSE691C295KWFPXGSD", "agent1_id": "01H5TNE5PMBJ9VHH51YC0BB64C", "agent2_id": "01H5TNE5Q1J7Z7Q12WA1W90MR9", "relationship": "2", "background_story": "Rafael Cortez and Jaxon Prentice met during a community event where Rafael was invited as a guest speaker. Jaxon, being an investigative journalist, was covering the event. Both being outgoing individuals, they hit it off and became acquaintances. They share a mutual respect for each other's professions, but their relationship has not deepened due to their low agreeableness and differing moral values."}
+{"relationship_id": "01H6HH9606VEK2HTMAASWZ351T", "agent1_id": "01H5TNE5PJTHMQ1Q3T398YN990", "agent2_id": "01H5TNE5PC6YGRH72RQAM862JH", "relationship": "1", "background_story": "Sasha Ramirez and Lily Greenberg have crossed paths numerous times due to their professional lives. Sasha, as a police officer, has often had to interact with Lily, a lawyer, in various cases. They have developed a professional acquaintance over time. They share a mutual understanding and respect due to their shared values of authority and loyalty. However, they keep their interactions strictly professional, hence they can be regarded as acquaintances."}
+{"relationship_id": "01H6HH3SW3ZZ4DFS7XKXV1E98J", "agent1_id": "01H5TNE5PT8KW11GZ99Q0T43V4", "agent2_id": "01H5TNE5PGWN8VGVAYDBKPN2TV", "relationship": "1", "background_story": "Esmeralda Solis, the District Attorney, and Noah Davis, a coach, met at a charity ballroom dance event. They know each other by name but haven't had extensive interactions beyond the event."}
+{"relationship_id": "01H6HH3KG4M9E1J5H25WHQG6NN", "agent1_id": "01H5TNE5PT06B3QPXJ65HHACV7", "agent2_id": "01H5TNE5P6KZKR2AEY6SZB83H0", "relationship": "4", "background_story": "Donovan and Oliver met at a local cafe where they both enjoy spending their leisure time. They have shared a few conversations talking about their interests and work. Donovan appreciates Oliver's meticulousness and analytical thinking, while Oliver admires Donovan's skills in coding. They have not developed a close friendship but are familiar with each other's names and backgrounds."}
+{"relationship_id": "01H6HH7R9RYGSTB3DHEDTJDT4F", "agent1_id": "01H5TNE5PW9SZFM058Z8P7PR5C", "agent2_id": "01H5TNE5P5EP6YJKPAT92ENQS6", "relationship": "1", "background_story": "Finnegan O'Malley and Emily Harrison crossed paths at their local library where Emily works. Finnegan was looking for books on painting techniques when Emily guided him to the right section. Since then, they occasionally meet at the library and share their interests in art and literature. They respect each other's introverted nature and enjoy their brief, but meaningful conversations."}
+{"relationship_id": "01H6HHAQTXS2CWY2KZM1VYWTWY", "agent1_id": "01H5TNE5PQ00AJVSSVB9V2VA9K", "agent2_id": "01H5TNE5PFB4W65DF8FRPDMET5", "relationship": "4", "background_story": "Eli Dawson and Sophia Brown cross paths frequently in their respective fields of psychiatry and psychology. They recognize each other and know each other by name, but have not yet had the opportunity to develop a deeper relationship."}
+{"relationship_id": "01H6HHCG06F1P64BJTRNVPEE8M", "agent1_id": "01H5TNE5PW9SZFM058Z8P7PR5C", "agent2_id": "01H5TNE5PM4XE5NN12WCGTZSDW", "relationship": "0", "background_story": "Finnegan O'Malley and Gwen Pierce met at a charity event where Gwen was donating some of her knitted creations. They found common ground in their shared love for charity and have been acquaintances ever since. Despite their different professions and personalities, their shared high level of agreeableness allows them to respect and understand each other."}
+{"relationship_id": "01H6HHP7X0AWDK13FB90BZ68Q8", "agent1_id": "01H5TNE5PW9SZFM058Z8P7PR5C", "agent2_id": "01H5TNE5P7RVY0TYX8VTCXABR6", "relationship": "2", "background_story": "Finnegan, the introverted lighthouse keeper, and Sophia, the exuberant personal trainer, first met at a local community event. Despite their differences in lifestyle and personality, they bonded over their shared love for the outdoors. Finnegan's calm demeanor and Sophia's vibrant energy balance each other out, making them good acquaintances. Sophia appreciates Finnegan's detailed approach to life, while Finnegan admires Sophia's spontaneous nature."}
+{"relationship_id": "01H6HHGKPM5B2BN7A3DX7WWNCW", "agent1_id": "01H5TNE5PWZ5PNDTGKDYRY36PQ", "agent2_id": "01H5TNE5P5EP6YJKPAT92ENQS6", "relationship": "1", "background_story": "Giselle and Emily met at a local art exhibition where Giselle's work was being displayed. Emily, an avid reader and writer, was drawn to the stories Giselle's art told. After a conversation about their shared values of care and fairness, they quickly became friends. They often meet in the local park, where Giselle photographs while Emily writes. They respect each other's secrets - Emily's secret romance novels and Giselle's illegal art business."}
+{"relationship_id": "01H6HHC2PPBHM3BBFBC8VVYQD5", "agent1_id": "01H5TNE5PBXGRD41HXQC1ZXHVN", "agent2_id": "01H5TNE5PT8KW11GZ99Q0T43V4", "relationship": "4", "background_story": "Ethan Johnson, a famous chef who excels in Italian cuisine, knows Esmeralda Solis, a District Attorney with a penchant for ballroom dancing, by name. They cross paths occasionally at community events and social gatherings, often engaging in brief conversations about their shared interest in power and authority, but their relationship is yet to evolve beyond a casual acquaintance."}
+{"relationship_id": "01H6HH6ZC55BS37M8G2TAWJA28", "agent1_id": "01H5TNE5PBXGRD41HXQC1ZXHVN", "agent2_id": "01H5TNE5P8F9NJ2QK2YP5HPXKH", "relationship": "3", "background_story": "Ethan Johnson, a successful chef famous for his Italian cuisine met Ava Martinez at a college event that he was catering. They struck up a conversation about their shared interest in creative expressions - Ethan's culinary art and Ava's poetry. Their shared openness to experience and extraversion made them instantly click. They've kept in touch, occasionally discussing Ethan's secret romance novel drafts and Ava's latest social activism efforts. They respect each other's secrets and have become good acquaintances over time."}
+{"relationship_id": "01H6HHRBCYJY6CGYCV2QVQ1C2P", "agent1_id": "01H5TNE5PKW8P500417PMSGSAC", "agent2_id": "01H5TNE5PR54W6SFXKNQWKGPAS", "relationship": "5", "background_story": "Miles and Baxter frequently cross paths at the local gardening supply shop where they share advice and tips about their respective gardens. Their shared love for plants has led them to develop a friendly rapport, although they still consider each other more of acquaintances than close friends."}
+{"relationship_id": "01H6HHQQK97D93R1NXTXFF5Z3Z", "agent1_id": "01H5TNE5PY896ASNX8XGQA6AE0", "agent2_id": "01H5TNE5PR54W6SFXKNQWKGPAS", "relationship": "1", "background_story": "Hendrick Heinz and Baxter Sterling are neighbors. They know each other by name, often seeing each other during their respective routines. Hendrick is often intrigued by the unusual plants in Baxter's backyard, while Baxter has attended a few of Hendrick's wine tasting events, appreciating the tradition and sense of achievement in the culinary world."}
+{"relationship_id": "01H6HHH1JQH3BMQR8XVXR96517", "agent1_id": "01H5TNE5PBKCFDAK6293NKYJ4D", "agent2_id": "01H5TNE5PGWN8VGVAYDBKPN2TV", "relationship": "3", "background_story": "Ava Thompson and Noah Davis met at a local community event. Ava, being an architect, was intrigued by Noah's extensive knowledge of structures and designs, which he acquired from having a swimming pool. Noah, on the other hand, found Ava's samoyed adorable. They got along well due to their shared moral value of care/harm and similar personality traits, leading to an amicable acquaintance. However, they are not aware of each other's secrets - Ava's hidden tattoo and Noah's secret identity as a stand-up comedian."}
+{"relationship_id": "01H6HHWGX2P5XPGNPH8FENYQZS", "agent1_id": "01H5TNE5P83CZ1TDBVN74NGEEJ", "agent2_id": "01H5TNE5P8F9NJ2QK2YP5HPXKH", "relationship": "0", "background_story": "Ava met William at a food festival where he was showcasing his culinary skills. She was drawn to his passion for food and they had a brief conversation about their shared love for trying out new things. They have bumped into each other a few times since then, but they are still just acquaintances."}
+{"relationship_id": "01H6HH3320XXZ0CA108XKQZ8CA", "agent1_id": "01H5TNE5PDV7WZ0C5KTGGXX1NR", "agent2_id": "01H5TNE5PPK39HR52G61PQ5KQ7", "relationship": "1", "background_story": "Isabelle and Zane often run into each other at local music venues where Zane can be found playing his guitar. They know each other by name but haven't spent much time together. Their shared openness to experience and high levels of extraversion could potentially lead to a deeper relationship in the future."}
+{"relationship_id": "01H6HHDCD4BFAPHVMXJBETT06B", "agent1_id": "01H5TNE5P83CZ1TDBVN74NGEEJ", "agent2_id": "01H5TNE5P6KZKR2AEY6SZB83H0", "relationship": "1", "background_story": "William and Oliver have crossed paths several times at various city events. They know each other by name and have engaged in brief conversations about their respective professions. Despite their different occupations, they share a high level of conscientiousness and extraversion. However, they haven't formed a close friendship due to their low agreeableness."}
+{"relationship_id": "01H6HHJCK7W485FGAEA3ZXW3R1", "agent1_id": "01H5TNE5P7RVY0TYX8VTCXABR6", "agent2_id": "01H5TNE5PHQKQYWS9ZS2JVEYFS", "relationship": "1", "background_story": "Sophia James and Lena Goodwin have been best friends for years. Sophia, the spontaneous personal trainer, often encourages Lena, the reserved architect, to step out of her comfort zone and enjoy life more. Despite their opposite personalities, they have found a strong bond in their shared values of fairness and care. However, their friendship is complicated by their secrets - Sophia is secretly in love with Lena's husband, while Lena once stole her best friend's boyfriend."}
+{"relationship_id": "01H6HHNRAT6KH11Z0FF3W0N41C", "agent1_id": "01H5TNE5P7RVY0TYX8VTCXABR6", "agent2_id": "01H5TNE5PAATSHM0K9ACWKN79P", "relationship": "1", "background_story": "Sophia and Benjamin met during a hiking trip organized by a local environmental group. Despite their differences, they connected over their mutual love for the outdoors and became good friends. Sophia often helps Benjamin with his physical fitness, while Benjamin inspires Sophia with his passion for environmental activism."}
+{"relationship_id": "01H6HH456AC4XKPFX54MHHTC7Y", "agent1_id": "01H5TNE5PWZ5PNDTGKDYRY36PQ", "agent2_id": "01H5TNE5PJRM958QWP3BHWY9DY", "relationship": "2", "background_story": "Giselle and Micah met at an art exhibition where Giselle was showcasing their photography. Micah, being an art enthusiast himself, praised Giselle's work. They became acquaintances, often discussing art and photography. However, they are unaware of each other's secrets."}
+{"relationship_id": "01H6HHGSPKAT4BXD5HN4TA9HVE", "agent1_id": "01H5TNE5P7VW4DY1KB09FZE730", "agent2_id": "01H5TNE5P6KZKR2AEY6SZB83H0", "relationship": "2", "background_story": "Liam and Oliver live in the same neighborhood. They know each other by name and would stop for a chat whenever they cross paths during Liam's dog walks or Oliver's trips to his painting studio. Their conversations are usually short and centered around neighborhood happenings, their pets, or Oliver's latest painting."}
+{"relationship_id": "01H6HHW19SCA49KKHCKRHA91ZW", "agent1_id": "01H5TNE5PT06B3QPXJ65HHACV7", "agent2_id": "01H5TNE5PBKCFDAK6293NKYJ4D", "relationship": "3", "background_story": "Donovan Reeves and Ava Thompson met during a tech conference where Donovan was participating in a coding competition. Despite their different careers, they found a common ground in their love for design and architecture. Donovan, being a software developer, has a keen interest in architectural design which is Ava's area of expertise. Ava, on the other hand, is fascinated by the intricacies of coding. They have kept in touch ever since, occasionally asking each other for professional advice."}
+{"relationship_id": "01H6HHKX43YA0G345EBNM8KCEY", "agent1_id": "01H5TNE5PDV7WZ0C5KTGGXX1NR", "agent2_id": "01H5TNE5PT06B3QPXJ65HHACV7", "relationship": "2", "background_story": "Isabelle and Donovan met at a local coffee shop where Isabelle works part-time. Donovan, a regular customer, often spent his after-work hours at the coffee shop, working on his personal coding projects. They became acquainted over time, discussing shared interests in liberty and the benefits of obedience. Despite their high openness to experience, their relationship remained as acquaintances due to Donovan's low extroversion and agreeableness."}
+{"relationship_id": "01H6HHR53ADAE4T00R92151BN2", "agent1_id": "01H5TNE5PM4XE5NN12WCGTZSDW", "agent2_id": "01H5TNE5PAATSHM0K9ACWKN79P", "relationship": "5", "background_story": "Gwen Pierce and Benjamin Jackson are both passionate about social justice and have crossed paths a few times at various charity events. While they aren't close friends, they have a mutual respect for each other's work and share a common understanding of the values of liberty and justice."}
+{"relationship_id": "01H6HHTW04GHNK5NCMP99MJW0Q", "agent1_id": "01H5TNE5PN656EADK59K4DG793", "agent2_id": "01H5TNE5PFB4W65DF8FRPDMET5", "relationship": "3", "background_story": "Naomi Fletcher and Sophia Brown met at a local coding boot camp where Sophia was taking part as a student. Naomi was a mentor in the boot camp, helping Sophia enhance her skills in software development. They have kept in touch since then, but they are not very close due to their respective secret lives."}
+{"relationship_id": "01H6HH6MKQK389KP433DWAEXXG", "agent1_id": "01H5TNE5PBKCFDAK6293NKYJ4D", "agent2_id": "01H5TNE5PY896ASNX8XGQA6AE0", "relationship": "2", "background_story": "Ava and Hendrick got to know each other at one of Hendrick's wine tasting events. Ava, being an architect, was interested in the building where the event was held and Hendrick, being the host, was more than happy to show her around. They share a professional relationship where they discuss their respective fields and occasionally share insights about their work."}
+{"relationship_id": "01H6HHA81GP4ZFZBW492R8Z305", "agent1_id": "01H5TNE5PPK39HR52G61PQ5KQ7", "agent2_id": "01H5TNE5P83CZ1TDBVN74NGEEJ", "relationship": "2", "background_story": "Zane Bennett and William Brown met at a local music and food festival. Zane was captivated by William's culinary skills while William admired Zane's creativity. They share a mutual respect for each other's talents, but their relationship has remained as acquaintances due to their busy schedules."}
+{"relationship_id": "01H6HH5E3W5AYGQSBDTTHA2DZ5", "agent1_id": "01H5TNE5PFT9HH0WRT6W1NY5GZ", "agent2_id": "01H5TNE5PDV7WZ0C5KTGGXX1NR", "relationship": "4", "background_story": "Leo Williams and Isabelle Martinez know each other by name, as Isabelle occasionally serves coffee to Leo at the coffee shop where she works part-time. However, they are not close and do not know much about each other's personal lives."}
+{"relationship_id": "01H6HHWQPG75VEDETPDJAQ63CN", "agent1_id": "01H5TNE5PT06B3QPXJ65HHACV7", "agent2_id": "01H5TNE5PSDH2H6JXYZ9ZRG7A4", "relationship": "5", "background_story": "Donovan and Calista met at a local tech and art exhibition, where Donovan was showcasing a software he designed and Calista was displaying her clothing line. Despite their different fields, they found common ground in their shared values of self-direction and their secret alter egos. Since then, they have been acquaintances, occasionally meeting at such events, respecting each other's work but not pursuing a deeper friendship due to their contrasting personalities."}
+{"relationship_id": "01H6HHQ7KBJK96Z3Z0BTWRDMCJ", "agent1_id": "01H5TNE5PKW8P500417PMSGSAC", "agent2_id": "01H5TNE5Q1J7Z7Q12WA1W90MR9", "relationship": "5", "background_story": "nan"}
+{"relationship_id": "01H6HHH73EH8G479ZZQ13RZ463", "agent1_id": "01H5TNE5PP870BS5HP2FPPKS2Y", "agent2_id": "01H5TNE5PQ00AJVSSVB9V2VA9K", "relationship": "1", "background_story": "Jasmine and Eli are both members of the same book club. Their interests differ greatly and they don't have much in common, hence they only know each other by name."}
+{"relationship_id": "01H6HH6CX2JEV6JY3SX553SJB1", "agent1_id": "01H5TNE5PN656EADK59K4DG793", "agent2_id": "01H5TNE5Q1QG5SBJ8HV7GJ0FS3", "relationship": "5", "background_story": "Naomi and Imelda met at a tech conference where they were both guest speakers. Despite their different fields, they found common ground discussing their love of problem-solving and analytical thinking. Their similar introverted personalities made them comfortable in each other's presence. They kept in touch professionally after the conference and occasionally share their thoughts on each other's works. They respect each other's intelligence and dedication to their fields but are not close friends."}
+{"relationship_id": "01H6HHSQ3BN7210MMAR99PY205", "agent1_id": "01H5TNE5PJRM958QWP3BHWY9DY", "agent2_id": "01H5TNE5PGWN8VGVAYDBKPN2TV", "relationship": "2", "background_story": "Micah Stevens and Noah Davis met at a local charity event where Noah was performing as a stand-up comedian. Micah, being an extravert, approached Noah after his performance and they quickly discovered that they both have a high level of openness to experience. Despite their difference in decision-making styles, with Micah being impulsive and Noah being cautious, they continue to cross paths at various events and have developed a mutual understanding and respect for each other, classifying them as acquaintances."}
+{"relationship_id": "01H6HHQYSRP0KTBE28V9JMS791", "agent1_id": "01H5TNE5P90FYSTBMW5DG5ERCG", "agent2_id": "01H5TNE5PDTDGA0BPYKBYFTHDY", "relationship": "4", "background_story": "Isabella, a devoted and empathetic veterinarian, often treats injured animals in her basement. One time, she found a rare bird with a broken wing and shared it on her community page, hoping to find any information about its origin. Ethan, a software developer, who is also a nature enthusiast, saw Isabella's post and contacted her offering his help. Since then, they know each other by name but their interaction was limited to that incident."}
+{"relationship_id": "01H6HH37ZTXREBSS60DR1FM5EB", "agent1_id": "01H5TNE5PQ00AJVSSVB9V2VA9K", "agent2_id": "01H5TNE5P98J20AEW94XQ0KC35", "relationship": "3", "background_story": "Eli Dawson and Ethan Smith are both members of an online book club focusing on history and technology. They know each other by name from the discussions and book reviews they have shared on the club's forum. However, they have not yet met in person or developed a deep personal connection."}
+{"relationship_id": "01H6HH636YJB4WPQXNQS0BYGNA", "agent1_id": "01H5TNE5PFT9HH0WRT6W1NY5GZ", "agent2_id": "01H5TNE5PSDH2H6JXYZ9ZRG7A4", "relationship": "1", "background_story": "Leo and Calista met at a charity run where Calista was participating and Leo was volunteering. Despite their differences, they quickly became friends. They often meet up for coffee and discuss their lives, although they both have secrets they haven\u2019t yet shared with each other. Leo has a secret child from a previous relationship and Calista lives a double life as an underground street artist."}
+{"relationship_id": "01H6HHG59SC6S2FJ8H3RXCXHH3", "agent1_id": "01H5TNE5PC2DP6RKG0KNQ4D5GE", "agent2_id": "01H5TNE5P5EP6YJKPAT92ENQS6", "relationship": "4", "background_story": "nan"}
+{"relationship_id": "01H6HHMN3993WZT8VVJ9X36TA6", "agent1_id": "01H5TNE5PWZ5PNDTGKDYRY36PQ", "agent2_id": "01H5TNE5PPK39HR52G61PQ5KQ7", "relationship": "0", "background_story": "Giselle and Zane met in an art exhibition where Zane was fascinated by Giselle's photography. They bonded over their shared interest in arts and high value of fairness and achievement. They quickly became acquaintances, maintaining a professional relationship that is both collaborative and creative."}
+{"relationship_id": "01H6HHK59QQ1Q4YM80NJKBZVFJ", "agent1_id": "01H5TNE5PKW8P500417PMSGSAC", "agent2_id": "01H5TNE5P83CZ1TDBVN74NGEEJ", "relationship": "2", "background_story": "Miles Hawkins and William Brown both work in the culinary field. They occasionally cross paths in various food events, and have developed mutual respect for each other's cooking styles. Despite their contrasting personalities, they share a common passion for cooking. Miles, with his impulsive decision-making, is often intrigued by William's strategic approach to cooking. Simultaneously, William appreciates Miles' creativity and spontaneity in the kitchen. They are not close friends but are more than just acquaintances in the culinary world."}
+{"relationship_id": "01H6HHBV6J7HAZ5K0DZD68Y9KA", "agent1_id": "01H5TNE5P7RVY0TYX8VTCXABR6", "agent2_id": "01H5TNE5PGWN8VGVAYDBKPN2TV", "relationship": "1", "background_story": "Sophia and Noah know each other through their professions in the fitness industry. Sophia, as a personal trainer, frequently collaborates with Noah, a coach, on various fitness events and programs. Even though they share a professional relationship, they haven't become close friends due to their different personality traits and decision-making styles. Sophia is more spontaneous and pleasure-seeking, while Noah is more cautious and reserved. Their interaction mostly revolves around fitness discussions and professional collaborations."}
+{"relationship_id": "01H6HHFP08NA6VZYADF08TN9GF", "agent1_id": "01H5TNE5PC2DP6RKG0KNQ4D5GE", "agent2_id": "01H5TNE5PY896ASNX8XGQA6AE0", "relationship": "0", "background_story": "Samuel Anderson and Hendrick Heinz know each other through their mutual passion for cooking. Samuel, being a software developer, designed an app for food recipes where Hendrick, as a chef, contributed some of his unique recipes. They share an acquaintanceship, mostly engaging in discussions about food and recipes. They both share Hedonism as a personal value, further strengthening their bond."}
+{"relationship_id": "01H6HH9QG5NM5CRGKWG76GMMZ8", "agent1_id": "01H5TNE5P6KZKR2AEY6SZB83H0", "agent2_id": "01H5TNE5PR54W6SFXKNQWKGPAS", "relationship": "3", "background_story": "Oliver and Baxter are both residents of the same neighborhood. Although they don't interact much due to their contrasting personalities and interests, they are aware of each other's occupations and hobbies. Oliver, the architect, admires Baxter's unique collection of plants, and Baxter often sees Oliver painting in his free time. Despite their differences, they have developed a mutual respect for each other as they both value tradition and security in their own ways."}
+{"relationship_id": "01H6HH1PS3AZDCS9KBBBCWM9T0", "agent1_id": "01H5TNE5PT06B3QPXJ65HHACV7", "agent2_id": "01H5TNE5PHQKQYWS9ZS2JVEYFS", "relationship": "2", "background_story": "Donovan and Lena met at a technology conference where Donovan was participating in a coding competition and Lena was presenting her latest architectural design. Despite their different fields, they found common ground in their love for analytical problem-solving. They've kept in touch since then, often exchanging ideas and thoughts on their respective fields."}
+{"relationship_id": "01H6HHEP4V9S5J54NWPCFJYK19", "agent1_id": "01H5TNE5PW9SZFM058Z8P7PR5C", "agent2_id": "01H5TNE5P90FYSTBMW5DG5ERCG", "relationship": "2", "background_story": "Finnegan O'Malley and Isabella White both share a love for care and benevolence though in different ways. They met at a community event and became acquaintances due to their shared values and introverted personalities. Their conversations often revolve around their respective professions and their love for tranquility."}
+{"relationship_id": "01H6HHJ49V8RYJRD0VGNE13X6K", "agent1_id": "01H5TNE5PC2DP6RKG0KNQ4D5GE", "agent2_id": "01H5TNE5PRCAF1CK5ERS5MVZ22", "relationship": "5", "background_story": "Samuel Anderson and Amara Hartley met during a local community event where Samuel was showcasing his cooking skills and Amara was advocating for animal rights. Their conversations were brief and infrequent, but they have remembered each other by name since then. They share common values of conscientiousness and low extraversion, but differ in values regarding openness to experience and agreeableness. Due to this, they respect each other's differences and maintain a cordial acquaintance relationship."}
+{"relationship_id": "01H6HH77KBBVJXCW1RRN9FDZ1S", "agent1_id": "01H5TNE5P83CZ1TDBVN74NGEEJ", "agent2_id": "01H5TNE5PT8KW11GZ99Q0T43V4", "relationship": "5", "background_story": "William and Esmeralda crossed paths at a charity event where William was providing the catering. They engaged in a brief but lively conversation about their shared love of dance and since then, they occasionally bump into each other at various events and exchange pleasantries. They can be considered as acquaintances."}
+{"relationship_id": "01H6HH2CE5AJXE7K6YXENS6VMS", "agent1_id": "01H5TNE5PFT9HH0WRT6W1NY5GZ", "agent2_id": "01H5TNE5P7VW4DY1KB09FZE730", "relationship": "1", "background_story": "Leo Williams and Liam Johnson are fellow residents in their neighborhood. Leo knows Liam by name as they occasionally cross paths when Leo is playing with his daughter and Liam is walking his dog. They haven't had deep interactions yet, but they both value security, which could be a common ground for deeper friendship in the future."}
+{"relationship_id": "01H6HHC96NY35XECGPRHWXWQYC", "agent1_id": "01H5TNE5PFT9HH0WRT6W1NY5GZ", "agent2_id": "01H5TNE5PGWN8VGVAYDBKPN2TV", "relationship": "2", "background_story": "Leo Williams and Noah Davis met at a neighborhood community event. They often chat about their professions, and Noah has shown interest in Leo's dental expertise to maintain his oral health for his stand-up comedy career."}
+{"relationship_id": "01H6HHCN9SYC82VED97NWRK070", "agent1_id": "01H5TNE5PN656EADK59K4DG793", "agent2_id": "01H5TNE5PHQKQYWS9ZS2JVEYFS", "relationship": "3", "background_story": "Naomi met Lena at a local tech meetup event a few years ago. They found common ground in their shared introverted tendencies and their respective fields in software development and architecture, often having deep conversations about design and structure. However, their relationship has remained largely professional, with a mutual respect for each other's work, making them more acquaintances than friends."}
+{"relationship_id": "01H6HHPRFYPAPXA28BXVQXP1G6", "agent1_id": "01H5TNE5P7VW4DY1KB09FZE730", "agent2_id": "01H5TNE5PBXGRD41HXQC1ZXHVN", "relationship": "3", "background_story": "Liam and Ethan Johnson are father and son. As a retired police officer, Liam is proud of his son Ethan's achievements as a chef, despite their different career paths. They have a strong bond and respect each other's space and choices. They share a common last name and the same moral value of loyalty, which is evident in their family relationship."}
+{"relationship_id": "01H6HHT8SZY7JRAYKCFG900DB6", "agent1_id": "01H5TNE5PKW8P500417PMSGSAC", "agent2_id": "01H5TNE5PT8KW11GZ99Q0T43V4", "relationship": "0", "background_story": "Miles Hawkins and Esmeralda Solis know each other as they frequent the same social circles. Miles, being a chef, often caters events which Esmeralda attends as a District Attorney. Their interactions are mostly professional, and they have a mutual respect for each other's careers. However, they don't share much in common due to their different interests, with Miles being into gardening and Esmeralda into ballroom dancing."}
+{"relationship_id": "01H6HHTFAK2M0NAKZD8JK2XJVQ", "agent1_id": "01H5TNE5PMBJ9VHH51YC0BB64C", "agent2_id": "01H5TNE5PAZABGW79HJ07TACCZ", "relationship": "4", "background_story": "Rafael Cortez and Mia Davis meet regularly due to their active involvement in various social events. Their relationship is mainly professional, they respect each other's work and are aware of each other's reputation. They are both extraverted and have crossed paths at several community events. Rafael admires Mia's authoritative nature, while Mia appreciates Rafael's systematic approach."}
+{"relationship_id": "01H6HH8NSRPP0RPGNWDKP8XKNK", "agent1_id": "01H5TNE5PRCAF1CK5ERS5MVZ22", "agent2_id": "01H5TNE5PBXGRD41HXQC1ZXHVN", "relationship": "5", "background_story": "Amara Hartley and Ethan Johnson know each other as they frequented the same farmer\u2019s market. Amara, a wildlife biologist, admired the conscientious sourcing of ingredients Ethan, a chef, practices for his Italian cooking. They respect each other's occupations and have had a few brief interactions about their shared value of fairness and ethical practices."}
+{"relationship_id": "01H6HHSE05V428MAF33W1NM48S", "agent1_id": "01H5TNE5PRCAF1CK5ERS5MVZ22", "agent2_id": "01H5TNE5PAATSHM0K9ACWKN79P", "relationship": "1", "background_story": "Amara Hartley and Benjamin Jackson met at an environmental conference. Sharing a mutual passion for nature and animal rights, they quickly became friends. They often collaborate on projects and campaigns, with Amara utilizing her knowledge as a Wildlife Biologist and Benjamin using his skills and influence as an Environmental Activist. Despite their secrets, they've managed to maintain a strong bond due to their shared values and goals."}
+{"relationship_id": "01H6HHM46ZJ15QKFG3MPXVWW0D", "agent1_id": "01H5TNE5PQ00AJVSSVB9V2VA9K", "agent2_id": "01H5TNE5P83CZ1TDBVN74NGEEJ", "relationship": "0", "background_story": "Eli Dawson, a forensic psychiatrist, and William Brown, a chef, met during a book fair in their city. Eli was intrigued by a rare cookbook that William was holding. They struck up a conversation and found that they both appreciate strategic decision-making. Despite their contrasting personalities, they became acquaintances and occasionally share their interests and secrets."}
+{"relationship_id": "01H6HHD50P98V0WBQETNYEM2EF", "agent1_id": "01H5TNE5PJTHMQ1Q3T398YN990", "agent2_id": "01H5TNE5PQ00AJVSSVB9V2VA9K", "relationship": "1", "background_story": "Sasha Ramirez and Eli Dawson know each other professionally. Sasha, as a police officer, often works with Eli, who is a forensic psychiatrist, on various cases. Despite their professional interactions, they maintain a distant personal relationship, characterized by mutual respect but also an underlying tension due to differing moral values. Sasha's secret of covering up a crime for her brother could potentially strain their professional relationship if Eli, with his sharp, strategic decision-making, were to find out."}
+{"relationship_id": "01H6HH4JYJR63CRR8NT06RM15R", "agent1_id": "01H5TNE5P7VW4DY1KB09FZE730", "agent2_id": "01H5TNE5PHQKQYWS9ZS2JVEYFS", "relationship": "5", "background_story": "Liam Johnson and Lena Goodwin live in the same neighborhood. Liam, as a retired police officer, often walks his dog around the neighborhood, while Lena, an architect, is usually seen capturing pictures of the buildings and structures she designs. They often cross paths during their respective activities and have exchanged a few words. Their relationship is that of acquaintances."}
+{"relationship_id": "01H6HHAY6KRNBYX5J5RGN75M6D", "agent1_id": "01H5TNE5P7VW4DY1KB09FZE730", "agent2_id": "01H5TNE5PGWN8VGVAYDBKPN2TV", "relationship": "4", "background_story": "Liam Johnson, a retired police officer, and Noah Davis, a local coach, know each other by name as they often cross paths while Liam walks his dog and Noah does his morning jog."}
+{"relationship_id": "01H6HHRJKAG8BV345ARQ2CDCMP", "agent1_id": "01H5TNE5P90FYSTBMW5DG5ERCG", "agent2_id": "01H5TNE5PHQKQYWS9ZS2JVEYFS", "relationship": "1", "background_story": "Isabella and Lena met at a community event and found they had similar interests and moral values. Their shared love for care and harm principle has made them friends. Isabella's empathy towards animals and Lena's analytical approach to her architecture work has led to many interesting and enriching discussions between the two."}
+{"relationship_id": "01H6HHA0SQ1F51T240E18DRDSN", "agent1_id": "01H5TNE5PJRM958QWP3BHWY9DY", "agent2_id": "01H5TNE5Q1J7Z7Q12WA1W90MR9", "relationship": "0", "background_story": "Micah Stevens and Jaxon Prentice know each other by name only. They met during a community event where Jaxon was actively participating. Micah, being a social butterfly, introduced himself to Jaxon. Their common value of power sparked a brief discussion between the two, but their differing personalities kept them from forming a deeper connection."}
+{"relationship_id": "01H6HHHP3EWZW3FGXCZDVMVC32", "agent1_id": "01H5TNE5PJTHMQ1Q3T398YN990", "agent2_id": "01H5TNE5P5EP6YJKPAT92ENQS6", "relationship": "2", "background_story": "Sasha Ramirez and Emily Harrison both live in the same neighborhood. Sasha, being a police officer, often visits the local library for her case research where Emily works as a librarian. They know each other by name and occasionally engage in small talk about their shared interests in gardening and writing respectively. They are not close friends but share a mutual respect for each other's occupations and hobbies."}
+{"relationship_id": "01H6HHS103V2Q83NK5KZB27KQK", "agent1_id": "01H5TNE5PJTHMQ1Q3T398YN990", "agent2_id": "01H5TNE5PY896ASNX8XGQA6AE0", "relationship": "4", "background_story": "Sasha Ramirez and Hendrick Heinz know each other by name through local community events. Sasha, being a police officer, has attended Hendrick's wine tasting nights a few times to ensure safety, but they are not close friends. They share similar moral values, but their personalities and secrets ensure they maintain a certain distance."}
+{"relationship_id": "01H6HHQ06GNQNA7G59YSQ0AM8A", "agent1_id": "01H5TNE5P7RVY0TYX8VTCXABR6", "agent2_id": "01H5TNE5PAZABGW79HJ07TACCZ", "relationship": "2", "background_story": "Sophia met Mia when she started offering fitness classes at the high school where Mia works as a principal. Their relationship is purely professional, with Sophia being respectful of Mia's authority at work. They don't interact outside of school activities."}
+{"relationship_id": "01H6HHNB6HGE3X9X4NNPEX5CC5", "agent1_id": "01H5TNE5PC6YGRH72RQAM862JH", "agent2_id": "01H5TNE5PBKCFDAK6293NKYJ4D", "relationship": "2", "background_story": "Lily Greenberg and Ava Thompson met during a charity event that Lily secretly donated to. They both have a high level of conscientiousness and low extraversion which led them to connect in a reserved, yet meaningful way. Despite their different careers, they share a mutual respect and understanding, making them good acquaintances."}
+{"relationship_id": "01H6HH7G3CRGQ6ZR9T48AMXVK0", "agent1_id": "01H5TNE5PAZABGW79HJ07TACCZ", "agent2_id": "01H5TNE5PHQKQYWS9ZS2JVEYFS", "relationship": "5", "background_story": "Mia Davis, the high school principal, and Lena Goodwin, the architect, first met during a local council meeting where Lena was presenting her building designs. Despite their differences, they found common ground in their passion for their work and became acquaintances. They occasionally run into each other at community events and have brief, professional conversations."}
+{"relationship_id": "01H6HHB7WRTE72JMAKRCB0XRMN", "agent1_id": "01H5TNE5P8F9NJ2QK2YP5HPXKH", "agent2_id": "01H5TNE5PFB4W65DF8FRPDMET5", "relationship": "1", "background_story": "Ava Martinez and Sophia Brown met at a social justice rally on campus. Ava, being a college student who is passionate about social justice, was speaking at the rally. Sophia, a psychologist interested in the interplay of social issues and psychology, was attending the rally for research purposes. Despite their different backgrounds, they found common ground in their shared passion for universalism and quickly became friends. They often engage in deep discussions about their respective fields and learn a lot from each other. Their friendship is strong, but neither of them knows the other's secret - Ava's hidden bisexuality and Sophia's undercover work."}
+{"relationship_id": "01H6HHV2CT6D5ENYYHM4DYJGJQ", "agent1_id": "01H5TNE5PQ00AJVSSVB9V2VA9K", "agent2_id": "01H5TNE5P7VW4DY1KB09FZE730", "relationship": "0", "background_story": "Eli Dawson and Liam Johnson met during a court case where Eli was a forensic psychiatrist and Liam was a police officer involved in the case. Since then, they have maintained an acquaintance relationship, occasionally crossing paths in their professional lives and in the neighborhood where they both live."}
+{"relationship_id": "01H6HH3ECV4MYBFBA15657TJ7H", "agent1_id": "01H5TNE5PN656EADK59K4DG793", "agent2_id": "01H5TNE5PR54W6SFXKNQWKGPAS", "relationship": "1", "background_story": "Naomi Fletcher and Baxter Sterling share a common interest in the digital world. Naomi, being a Software Developer, had an online interaction with Baxter who unknowingly admires her secret alter ego. They know each other by names only, having communicated only in a virtual platform."}
+{"relationship_id": "01H6HHVATC35JMD8GPJ6HG8A8X", "agent1_id": "01H5TNE5PDTDGA0BPYKBYFTHDY", "agent2_id": "01H5TNE5PGWN8VGVAYDBKPN2TV", "relationship": "2", "background_story": "Oliver Smith and Noah Davis live in the same neighborhood. They know each other by name and frequently engage in friendly chats when they meet. Oliver, being an introverted policeman, admires Noah's secret talent as a stand-up comedian. On the other hand, Noah, a coach, appreciates Oliver's dedication to keeping their neighborhood safe. Their mutual respect and shared interests in each other's secret hobbies have helped form a casual acquaintance."}
+{"relationship_id": "01H6HHRRK8GCV866D5C3BGY381", "agent1_id": "01H5TNE5P6KZKR2AEY6SZB83H0", "agent2_id": "01H5TNE5PT8KW11GZ99Q0T43V4", "relationship": "3", "background_story": "Oliver Thompson and Esmeralda Solis are neighbors. They know each other by name but do not have a close relationship. Oliver has a hidden feeling for Esmeralda, but she is unaware of this due to her secret romantic relationships with several high-profile criminals."}
+{"relationship_id": "01H6HHVS9SY28PD9K0PNX4NA1H", "agent1_id": "01H5TNE5PY896ASNX8XGQA6AE0", "agent2_id": "01H5TNE5P98J20AEW94XQ0KC35", "relationship": "3", "background_story": "Hendrick and Ethan only know each other by name as they both frequent the same bookstore. Hendrick is interested in cookbooks while Ethan is usually found in the technology section. They have exchanged nods of acknowledgment but have never had a full conversation."}
+{"relationship_id": "01H6HH4ZGASB3N2RDQ8P8W1PF3", "agent1_id": "01H5TNE5PRCAF1CK5ERS5MVZ22", "agent2_id": "01H5TNE5Q1J7Z7Q12WA1W90MR9", "relationship": "4", "background_story": "Amara and Jaxon know each other by name through their shared involvement in community functions. Jaxon, being an investigative journalist, respects Amara's passion for wildlife preservation. However, they have never been closely acquainted due to their contrasting personalities and moral values."}
+{"relationship_id": "01H6HHNZJFV56PPYH3FZBEWTBJ", "agent1_id": "01H5TNE5PW9SZFM058Z8P7PR5C", "agent2_id": "01H5TNE5Q1QG5SBJ8HV7GJ0FS3", "relationship": "4", "background_story": "Finnegan and Oliver met at a local painting class. Despite their differences in personalities, they found common ground in their shared love for painting. Occasionally, they exchange thoughts on their artwork, but their interactions remain mostly cordial and brief due to Finnegan's introverted nature."}
+{"relationship_id": "01H6HHFE6MJSBBJHJ1YHZ5S06R", "agent1_id": "01H5TNE5P7RVY0TYX8VTCXABR6", "agent2_id": "01H5TNE5PR54W6SFXKNQWKGPAS", "relationship": "4", "background_story": "Sophia James and Baxter Sterling know each other as they both frequent the same local gym. Sophia, being a personal trainer, has given Baxter a few tips on his workout routine. Baxter, on the other hand, has shown Sophia some interesting antiques from his collection. Despite their contrasting personalities, they maintain a cordial acquaintance."}
+{"relationship_id": "01H6HHAH3CAC03EQCXF48K89X3", "agent1_id": "01H5TNE5P7RVY0TYX8VTCXABR6", "agent2_id": "01H5TNE5PQ00AJVSSVB9V2VA9K", "relationship": "2", "background_story": "Sophia and Eli met at a local history event that Eli was attending as a hobby and Sophia as a social gathering. They got acquainted due to their mutual interest in hedonism, despite their contrasting personalities and lifestyle. Eli, being older and more experienced, often gives Sophia advice, while Sophia helps Eli keep in shape with her personal training. They maintain an amicable relationship, respecting each other's personal space and values."}
+{"relationship_id": "01H6HHMWPF0ZQ31Q8Z2KM4R22B", "agent1_id": "01H5TNE5PAATSHM0K9ACWKN79P", "agent2_id": "01H5TNE5P98J20AEW94XQ0KC35", "relationship": "3", "background_story": "Benjamin and Ethan met at a technology and environment conference where Benjamin was a keynote speaker. Ethan, intrigued by Benjamin's idealistic approach, introduced himself after the talk. Though they share different views, they respect each other's values and occasionally exchange ideas on how technology can contribute to environmentalism."}
+{"relationship_id": "01H6HH3ZR7AM1AQQ4E5SBQ5M6W", "agent1_id": "01H5TNE5PAZABGW79HJ07TACCZ", "agent2_id": "01H5TNE5P90FYSTBMW5DG5ERCG", "relationship": "2", "background_story": "Mia Davis and Isabella White know each other through their community. Isabella provides veterinary services for Mia's cats. They have a professional relationship and often engage in small talk during visits. Their contrasting personality traits make them less likely to develop a deeper friendship."}
+{"relationship_id": "01H6HHQFQTWY57983SAKEK11KP", "agent1_id": "01H5TNE5PBKCFDAK6293NKYJ4D", "agent2_id": "01H5TNE5P98J20AEW94XQ0KC35", "relationship": "4", "background_story": "Ava Thompson and Ethan Smith are colleagues at a tech firm. Ava, an architect, frequently collaborates with Ethan, a software developer, on different projects. They know each other by name and occasionally engage in small talk. Despite their differing personalities and decision-making styles, they respect each other's professional skills and contributions."}
+{"relationship_id": "01H6HHMDPGGJYBR4AFMZC1KWMK", "agent1_id": "01H5TNE5PWZ5PNDTGKDYRY36PQ", "agent2_id": "01H5TNE5PE9RQGH86YM6MSWZMW", "relationship": "5", "background_story": "Giselle Rousseau and Mia Sanders met at a local art exhibition. Giselle was drawn to Mia's quiet observation of the art pieces, while Mia admired Giselle's vibrant enthusiasm. Both share a high level of conscientiousness and agreeableness, which has led to a mutual respect for each other. Their shared love for art, despite not being publicly known for Mia, has allowed them to form a connection that surpasses a mere acquaintance. However, their secret artistic indulgences, Giselle's forgery and Mia's hidden talent, are yet to be discovered by each other."}
+{"relationship_id": "01H6HHDVNK6GVY59X4NB38536W", "agent1_id": "01H5TNE5PC6YGRH72RQAM862JH", "agent2_id": "01H5TNE5PY896ASNX8XGQA6AE0", "relationship": "4", "background_story": "Isabella and Hendrick met in a local farmers market where Hendrick would get fresh produce for his restaurant. Isabella knowing Hendrick by his reputation as a chef, struck up a conversation about organic food and how it's beneficial for both humans and animals. Over time, they have become familiar faces to each other, sharing a nod and a smile whenever they cross paths."}
+{"relationship_id": "01H6HH9G050VKNXQ4ZACDWME57", "agent1_id": "01H5TNE5PW9SZFM058Z8P7PR5C", "agent2_id": "01H5TNE5PGWN8VGVAYDBKPN2TV", "relationship": "3", "background_story": "Finnegan O'Malley and Noah Davis met at a local art fair where Finnegan was showcasing his seascape paintings. Noah, being an outgoing personality, struck up a conversation with Finnegan, who is generally shy and introverted. They quickly found common ground in their love for the sea, and over time became acquaintances. Despite their contrasting personalities, they both share high conscientiousness and agreeableness, which somewhat balances their interaction. Noah is still unaware of Finnegan's past involvement in a cult, while Finnegan has no clue about Noah's secret life as a stand-up comedian."}
+{"relationship_id": "01H6HHHWXMZCVH8EFB0R31WC9C", "agent1_id": "01H5TNE5PBXGRD41HXQC1ZXHVN", "agent2_id": "01H5TNE5PR54W6SFXKNQWKGPAS", "relationship": "5", "background_story": "Ethan and Baxter are acquaintances. They met at a local flea market where Baxter was looking for antiques and Ethan was seeking inspiration for his Italian cooking. Despite their differing interests and personalities, they found common ground in their appreciation for quality and tradition. Ethan, intrigued by Baxter's collection of rare plants, occasionally consults him for unique ingredients. Baxter, in turn, enjoys Ethan's creative spin on traditional Italian food."}
+{"relationship_id": "01H6HHNHK9TA32BX9ZCHKSR4ZB", "agent1_id": "01H5TNE5PFT9HH0WRT6W1NY5GZ", "agent2_id": "01H5TNE5PY896ASNX8XGQA6AE0", "relationship": "3", "background_story": "Leo and Hendrick know each other by name through a mutual friend. They have met on a few occasions but they don't have a close relationship."}
+{"relationship_id": "01H6HHKN6GB4DRQ91FWVJ4RYPH", "agent1_id": "01H5TNE5PM4XE5NN12WCGTZSDW", "agent2_id": "01H5TNE5P98J20AEW94XQ0KC35", "relationship": "5", "background_story": "Gwen and Ethan first met at a charity event where they found common ground in their shared values of universalism and fairness. Since then, they occasionally run into each other at similar events and have brief, but meaningful conversations about their shared interests and values. Despite their different occupations, they maintain a mutual respect for each other's work and contributions to society."}
+{"relationship_id": "01H6HH8EX04RT73GHXKQXR18FX", "agent1_id": "01H5TNE5PWZ5PNDTGKDYRY36PQ", "agent2_id": "01H5TNE5Q1J7Z7Q12WA1W90MR9", "relationship": "5", "background_story": "Giselle and Jaxon have crossed paths multiple times at community events where Giselle was showcasing their art and Jaxon was covering the event for his news outlet. They know each other by name and have had brief conversations, but their relationship hasn't progressed beyond that of acquaintances due to their differing lifestyles and personalities."}
+{"relationship_id": "01H6HH8Z4PQDKF8S975YCE18Q6", "agent1_id": "01H5TNE5PDV7WZ0C5KTGGXX1NR", "agent2_id": "01H5TNE5PRCAF1CK5ERS5MVZ22", "relationship": "2", "background_story": "Isabelle Martinez and Amara Hartley crossed paths at a local park where Amara was working on a project related to wildlife. Isabelle, being a student with a high openness to experience, was intrigued by Amara's work. They started with casual conversations and later turned into regular meet-ups to discuss various topics, from animal rights to magic. Despite their different personalities and secrets, they managed to build a bond of acquaintance, driven by their shared high agreeableness and openness to experience."}
+{"relationship_id": "01H6HH4SWXN72SQW6SG2190EKM", "agent1_id": "01H5TNE5PWZ5PNDTGKDYRY36PQ", "agent2_id": "01H5TNE5Q1QG5SBJ8HV7GJ0FS3", "relationship": "2", "background_story": "Giselle and Imelda first met at a science and art fusion exhibition. Giselle was interested in Imelda's work, and despite their differences, they began to respect each other\u2019s passions. Their friendship remained casual, each of them holding a secret they could not share. They often meet for intellectual discussions and debates, but their relationship hasn't deepened due to their secret life."}
+{"relationship_id": "01H6HHKDT0Q2K44JCMJPM5ESMG", "agent1_id": "01H5TNE5PRCAF1CK5ERS5MVZ22", "agent2_id": "01H5TNE5PAZABGW79HJ07TACCZ", "relationship": "3", "background_story": "Amara Hartley and Mia Davis know each other by name through local community events. Mia, as the High School Principal, has heard about Amara's work as a Wildlife Biologist. However, their interactions have been limited."}
+{"relationship_id": "01H6HHTP53EQ0AW9QR0H6JFBVV", "agent1_id": "01H5TNE5PN656EADK59K4DG793", "agent2_id": "01H5TNE5PJRM958QWP3BHWY9DY", "relationship": "4", "background_story": "Samuel and Micah met in a cooking class that Samuel was taking for fun. They bonded over their shared love of food, and have kept in touch since then. They occasionally meet up for meals or cooking sessions, but do not consider each other close friends."}
+{"relationship_id": "01H6HH87G7PPH74A5R51EYVEEF", "agent1_id": "01H5TNE5PAZABGW79HJ07TACCZ", "agent2_id": "01H5TNE5PAATSHM0K9ACWKN79P", "relationship": "3", "background_story": "Mia Davis, the high school principal, first met Benjamin Jackson, the environmental activist, during a school seminar on climate change. Mia was intrigued by Benjamin's passion and commitment to the cause, despite being the descendant of a wealthy oil tycoon. Benjamin, on the other hand, found Mia's authoritarian approach and her secret rebellious past interesting. Since then, they have maintained an acquaintance relationship, occasionally crossing paths at community events."}
+{"relationship_id": "01H6HHJVSKJFTF1Q91M10SWXCN", "agent1_id": "01H5TNE5PMBJ9VHH51YC0BB64C", "agent2_id": "01H5TNE5PE9RQGH86YM6MSWZMW", "relationship": "2", "background_story": "Rafael Cortez and Mia Sanders work together in the same hospital, Rafael as a surgeon and Mia as a nurse. Rafael is aware of Mia's existence but they have never gone beyond professional acknowledgment and courtesy. Their personalities contrast, and their interactions remain limited to professional necessities. Rafael's outgoing nature doesn't particularly mesh with Mia's introverted disposition. They have a mutual respect for each other's work, but their relationship hasn't evolved beyond that of colleagues."}
+{"relationship_id": "01H6HHHF08HMHH2K5SYGPRR3MJ", "agent1_id": "01H5TNE5PKW8P500417PMSGSAC", "agent2_id": "01H5TNE5PPK39HR52G61PQ5KQ7", "relationship": "2", "background_story": "Miles Hawkins and Zane Bennett met at a local farmer's market where Miles was sourcing fresh ingredients for his restaurant. They bonded over their love for music and gardening. Miles, the chef, often seeks Zane's creative input on the presentation of his dishes. They respect each other's professions and share a mutual understanding of each other's personal values, however, they only meet occasionally and hence, are more of acquaintances than close friends."}
+{"relationship_id": "01H6HH5WK102XD4SVB654ZVFDX", "agent1_id": "01H5TNE5PC6YGRH72RQAM862JH", "agent2_id": "01H5TNE5P90FYSTBMW5DG5ERCG", "relationship": "0", "background_story": "Lily Greenberg and Isabella White first met at a charity event where Lily was making one of her anonymous donations. As both women are introverted, they did not make immediate connections, but over time, they came to recognize and respect each other's work - Lily as a dedicated lawyer, and Isabella as a compassionate veterinarian. Despite their differences, their shared value for conscientiousness has helped them form a mutual acquaintance."}
+{"relationship_id": "01H6HHE8TNK4FEYQP2X5ZK5009", "agent1_id": "01H5TNE5PDTDGA0BPYKBYFTHDY", "agent2_id": "01H5TNE5PN656EADK59K4DG793", "relationship": "2", "background_story": "Oliver Smith and Naomi Fletcher know each other through a local community gardening group where Oliver secretly participates. Despite their different occupations as a police officer and software developer, their shared interest in gardening brings them together. Oliver appreciates Naomi's dedication and mentoring skills as she often helps less experienced gardeners, while Naomi respects Oliver's conscientiousness and systematic approach in maintaining the community garden. They are not close friends, but they know each other well enough to have a friendly chat whenever they meet at the gardening group."}
+{"relationship_id": "01H6HHCWKD6X55Y1DFS2KTXJMP", "agent1_id": "01H5TNE5PC6YGRH72RQAM862JH", "agent2_id": "01H5TNE5PAZABGW79HJ07TACCZ", "relationship": "2", "background_story": "Lily Greenberg and Mia Davis worked together in a legal case where Lily was representing a client against the high school where Mia is a principal. Their professional relationship was established, but they had disagreements due to their differing values and decision-making styles. Despite their differences, they had mutual respect for each other's authority and professionalism."}
+{"relationship_id": "01H6HHVJG5T756HRDV7X139KG6", "agent1_id": "01H5TNE5PN656EADK59K4DG793", "agent2_id": "01H5TNE5PJRM958QWP3BHWY9DY", "relationship": "3", "background_story": "Naomi and Micah met at a technology conference where they were both speakers. Given their common interest in technology and intuition-driven decision making, they quickly became acquaintances. They occasionally meet up to discuss their latest projects and shared interests. Despite their contrasting personalities, with Naomi being introverted and Micah being extraverted, they respect each other's perspectives and enjoy their stimulating discussions."}
+{"relationship_id": "01H6HHEZPKEYQW0JG5NC94DG7K", "agent1_id": "01H5TNE5PDV7WZ0C5KTGGXX1NR", "agent2_id": "01H5TNE5P8F9NJ2QK2YP5HPXKH", "relationship": "3", "background_story": "Isabelle and Ava Martinez, both 22 years old, are actually twin sisters. They share a lot in common: they are both students, highly extraverted, agreeable yet highly neurotic. Isabelle works part-time in a coffee shop while secretly studying magic, and Ava is known for their involvement in social movements on campus and has a secret passion for writing and drawing. Despite their differences, they have a strong bond and are always there for each other."}
+{"relationship_id": "01H6HHT06HP2HKYQRW2F0H4HK4", "agent1_id": "01H5TNE5PMBJ9VHH51YC0BB64C", "agent2_id": "01H5TNE5P6KZKR2AEY6SZB83H0", "relationship": "0", "background_story": "Rafael Cortez and Oliver Thompson, both 45 and sharing a similar personality type, know each other through their shared interest in visiting historical landmarks, a hobby they discovered during a local history society event. Their relationship is mostly limited to discussions on this topic during the society's meetings. Rafael appreciates Oliver's meticulous nature and analytical thinking, which often leads to insightful conversations about the historical sites they visit. However, they are not close friends due to their low agreeableness and focus on their individual interests and careers."}
+{"relationship_id": "01H6HHE1K77Y4KWP2DQBVQWH97", "agent1_id": "01H5TNE5P98J20AEW94XQ0KC35", "agent2_id": "01H5TNE5PGWN8VGVAYDBKPN2TV", "relationship": "0", "background_story": "Ethan and Noah met at a technology conference a few years ago. They connected over common interests in technology and personal development, but due to their differing personalities, they've never progressed beyond being acquaintances."}
+{"relationship_id": "01H6HHS71WTW8ASBS4H9A0C1AZ", "agent1_id": "01H5TNE5PJTHMQ1Q3T398YN990", "agent2_id": "01H5TNE5PRCAF1CK5ERS5MVZ22", "relationship": "0", "background_story": "Sasha Ramirez and Amara Hartley are strangers to each other. Sasha is a dedicated police officer while Amara is a passionate wildlife biologist. Their paths have not crossed yet in the course of their respective duties."}
+{"relationship_id": "01H6HH56E86X1KTDR7C7MP96QQ", "agent1_id": "01H5TNE5PFT9HH0WRT6W1NY5GZ", "agent2_id": "01H5TNE5PRCAF1CK5ERS5MVZ22", "relationship": "1", "background_story": "Leo and Amara met at a community event where Leo was giving a talk about dental hygiene and Amara was representing her wildlife organization. They have only interacted a few times since then, so they only know each other by name."}
+{"relationship_id": "01H6HH2X1EZJE0DD5JEPCCSSHH", "agent1_id": "01H5TNE5PBXGRD41HXQC1ZXHVN", "agent2_id": "01H5TNE5PAATSHM0K9ACWKN79P", "relationship": "3", "background_story": "Ethan and Benjamin met at an environmental conference where Ethan was catering. They found they had a mutual respect for each other's professions but differ in their personal values. They occasionally meet to discuss their differing viewpoints."}
+{"relationship_id": "01H6HH5MKRA28C02AWCN9JD0E4", "agent1_id": "01H5TNE5P6KZKR2AEY6SZB83H0", "agent2_id": "01H5TNE5P98J20AEW94XQ0KC35", "relationship": "2", "background_story": "Oliver and Ethan live in the same neighborhood. They occasionally meet at community events, where they engage in brief conversations about their interests. Oliver is intrigued by Ethan's knowledge on tech advancements, while Ethan appreciates Oliver's artistic passion. Despite their differences, they maintain a cordial acquaintance."}
+{"relationship_id": "01H6HHWZPK8TAR1W2TF3A29F4D", "agent1_id": "01H5TNE5PDV7WZ0C5KTGGXX1NR", "agent2_id": "01H5TNE5PSDH2H6JXYZ9ZRG7A4", "relationship": "5", "background_story": "nan"}
+{"relationship_id": "01H6HHFXM3NS95ERRM08N73644", "agent1_id": "01H5TNE5P7VW4DY1KB09FZE730", "agent2_id": "01H5TNE5Q1J7Z7Q12WA1W90MR9", "relationship": "2", "background_story": "Liam Johnson, a retired police officer, and Jaxon Prentice, an investigative journalist, live in the same neighborhood. They often cross paths during their morning routines, with Liam walking his dog and Jaxon going for a jog. They've had a few casual conversations, mostly about local news and events, which has made them acquaintances."}
+{"relationship_id": "01H6HH2QQR1FARM527DJMHQM23", "agent1_id": "01H5TNE5PAZABGW79HJ07TACCZ", "agent2_id": "01H5TNE5P83CZ1TDBVN74NGEEJ", "relationship": "3", "background_story": "Mia Davis and William Brown first met at a school fundraiser where William was catering. They got along well due to their shared extraversion, even though they had some disagreements due to their contrasting values. They became acquaintances and would often chat when they cross paths at community events."}

requirements.txt

@@ -0,0 +1,148 @@
+absl-py==2.1.0
+accelerate==0.29.3
+aiofiles==23.2.1
+aiohttp==3.9.5
+aiosignal==1.3.1
+altair==5.3.0
+annotated-types==0.6.0
+anyio==3.7.1
+attrs==23.2.0
+beartype==0.14.1
+bitsandbytes==0.42.0
+certifi==2024.2.2
+cffi==1.16.0
+charset-normalizer==3.3.2
+click==8.1.7
+cloudpickle==3.0.0
+contourpy==1.2.1
+cryptography==42.0.5
+cycler==0.12.1
+dataclasses-json==0.6.4
+datasets==2.18.0
+dill==0.3.8
+distro==1.9.0
+Farama-Notifications==0.0.4
+ffmpy==0.3.2
+filelock==3.13.4
+fonttools==4.51.0
+frozenlist==1.4.1
+fsspec==2024.2.0
+gin-config==0.5.0
+gradio==4.27.0
+gradio_client==0.15.1
+greenlet==3.0.3
+gymnasium==0.29.1
+h11==0.14.0
+hiredis==2.3.2
+httpcore==1.0.5
+httpx==0.27.0
+huggingface-hub==0.22.2
+idna==3.7
+importlib_metadata==7.1.0
+importlib_resources==6.4.0
+Jinja2==3.1.3
+jsonpatch==1.33
+jsonpointer==2.4
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+kiwisolver==1.4.5
+langchain==0.1.16
+langchain-community==0.0.33
+langchain-core==0.1.44
+langchain-openai==0.0.5
+langchain-text-splitters==0.0.1
+langsmith==0.1.48
+litellm==1.35.12
+lxml==4.9.4
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+marshmallow==3.21.1
+matplotlib==3.8.4
+mdurl==0.1.2
+more-itertools==10.2.0
+mpmath==1.3.0
+multidict==6.0.5
+multiprocess==0.70.16
+mypy==1.9.0
+mypy-extensions==1.0.0
+names==0.3.0
+networkx==3.3
+numpy==1.26.4
+# nvidia-cublas-cu12==12.1.3.1
+# nvidia-cuda-cupti-cu12==12.1.105
+# nvidia-cuda-nvrtc-cu12==12.1.105
+# nvidia-cuda-runtime-cu12==12.1.105
+# nvidia-cudnn-cu12==8.9.2.26
+# nvidia-cufft-cu12==11.0.2.54
+# nvidia-curand-cu12==10.3.2.106
+# nvidia-cusolver-cu12==11.4.5.107
+# nvidia-cusparse-cu12==12.1.0.106
+# nvidia-nccl-cu12==2.19.3
+# nvidia-nvjitlink-cu12==12.4.127
+# nvidia-nvtx-cu12==12.1.105
+openai==1.22.0
+orjson==3.10.1
+packaging==23.2
+pandas==2.2.2
+pandas-stubs==2.2.1.240316
+peft==0.10.0
+pettingzoo==1.24.0
+pillow==10.3.0
+psutil==5.9.8
+pyarrow==15.0.2
+pyarrow-hotfix==0.6
+pycparser==2.22
+pydantic==2.7.0
+pydantic_core==2.18.1
+pydub==0.25.1
+Pygments==2.17.2
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-multipart==0.0.9
+python-ulid==1.1.0
+pytz==2024.1
+PyYAML==6.0.1
+redis==5.0.3
+referencing==0.34.0
+regex==2024.4.16
+requests==2.31.0
+rich==13.7.1
+rpds-py==0.18.0
+ruff==0.3.7
+safetensors==0.4.3
+scipy==1.13.0
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.1
+SQLAlchemy==2.0.29
+sseclient-py==1.8.0
+starlette==0.27.0
+sympy==1.12
+tabulate==0.9.0
+tenacity==8.2.3
+tiktoken==0.5.2
+tokenizers==0.19.1
+tomlkit==0.12.0
+toolz==0.12.1
+torch==2.2.2
+tqdm==4.66.2
+transformers==4.40.0
+# triton==2.2.0
+typer==0.12.3
+types-cffi==1.16.0.20240331
+types-pyOpenSSL==24.0.0.20240417
+types-pytz==2024.1.0.20240417
+types-redis==4.6.0.20240417
+types-setuptools==69.5.0.20240415
+types-tqdm==4.66.0.20240417
+typing-inspect==0.9.0
+typing_extensions==4.11.0
+tzdata==2024.1
+urllib3==2.2.1
+uvicorn==0.23.2
+websockets==11.0.3
+xxhash==3.4.1
+yarl==1.9.4
+zipp==3.18.1

sotopia_pi_generate.py

@@ -0,0 +1,256 @@
+import re
+import os
+from typing import TypeVar
+from functools import cache
+import logging
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
+from peft import PeftModel
+from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
+from langchain_community.chat_models import ChatLiteLLM
+from langchain.chains import LLMChain
+from langchain.output_parsers import PydanticOutputParser
+from langchain.prompts import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    PromptTemplate,
+)
+from langchain.schema import BaseOutputParser, OutputParserException
+from message_classes import ActionType, AgentAction
+from utils import format_docstring
+
+from langchain_callback_handler import LoggingCallbackHandler
+
+HF_TOKEN_KEY_FILE="./hf_token.key"
+if os.path.exists(HF_TOKEN_KEY_FILE):
+    with open(HF_TOKEN_KEY_FILE, "r") as f:
+        os.environ["HF_TOKEN"] = f.read().strip()
+
+OutputType = TypeVar("OutputType", bound=object)
+log = logging.getLogger("generate")
+logging_handler = LoggingCallbackHandler("langchain")
+
+def generate_action(
+    model_name: str,
+    history: str,
+    turn_number: int,
+    action_types: list[ActionType],
+    agent: str,
+    temperature: float = 0.7,
+) -> AgentAction:
+    """
+    Using langchain to generate an example episode
+    """
+    # try:
+        # Normal case, model as agent
+    template = """
+        Imagine you are {agent}, your task is to act/speak as {agent} would, keeping in mind {agent}'s social goal.
+        You can find {agent}'s goal (or background) in the 'Here is the context of the interaction' field.
+        Note that {agent}'s goal is only visible to you.
+        You should try your best to achieve {agent}'s goal in a way that align with their character traits.
+        Additionally, maintaining the conversation's naturalness and realism is essential (e.g., do not repeat what other people has already said before).\n
+        {history}.
+        You are at Turn #{turn_number}. Your available action types are
+        {action_list}.
+        Note: You can "leave" this conversation if 1. you have achieved your social goals, 2. this conversation makes you uncomfortable, 3. you find it uninteresting/you lose your patience, 4. or for other reasons you want to leave.
+
+        Please only generate a JSON string including the action type and the argument.
+        Your action should follow the given format:
+        {format_instructions}
+    """
+    return generate(
+        model_name=model_name,
+        template=template,
+        input_values=dict(
+            agent=agent,
+            turn_number=str(turn_number),
+            history=history,
+            action_list=" ".join(action_types),
+        ),
+        output_parser=PydanticOutputParser(pydantic_object=AgentAction),
+        temperature=temperature,
+    )
+    # except Exception as e:
+    #     print(e)
+    #     return AgentAction(action_type="none", argument="")
+
+@cache
+def prepare_model(model_name):
+    compute_type = torch.float16
+    
+    if model_name == 'cmu-lti/sotopia-pi-mistral-7b-BC_SR':
+        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1", model_max_length=4096)
+        model = AutoModelForCausalLM.from_pretrained(
+        "mistralai/Mistral-7B-Instruct-v0.1",
+        cache_dir="./.cache",
+        device_map='cuda'
+        )
+        model = PeftModel.from_pretrained(model, model_name).to("cuda")
+        
+    elif model_name == 'cmu-lti/sotopia-pi-mistral-7b-BC_SR_4bit':
+        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1", model_max_length=4096)
+        model = AutoModelForCausalLM.from_pretrained(
+        "mistralai/Mistral-7B-Instruct-v0.1",
+        cache_dir="./.cache",
+        device_map='cuda',
+        quantization_config=BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=compute_type,
+            )
+        )
+        model = PeftModel.from_pretrained(model, model_name[0:-5]).to("cuda")
+    
+    elif model_name == 'mistralai/Mistral-7B-Instruct-v0.1':
+        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1", model_max_length=4096)
+        tokenizer.model_max_length = 4096
+        model = AutoModelForCausalLM.from_pretrained(
+        "mistralai/Mistral-7B-Instruct-v0.1",
+        cache_dir="./.cache",
+        device_map='cuda'
+        )
+        
+    else:
+         raise RuntimeError(f"Model {model_name} not supported")
+     
+    return model, tokenizer
+
+def obtain_chain_hf(
+    model_name: str,
+    template: str,
+    input_variables: list[str],
+    temperature: float = 0.7,
+    max_retries: int = 6,
+    max_tokens: int = 2700
+) -> LLMChain:
+    human_message_prompt = HumanMessagePromptTemplate(
+        prompt=PromptTemplate(template=template, input_variables=input_variables)
+    )
+    chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])
+    model, tokenizer = prepare_model(model_name)
+    pipe = pipeline("text-generation", 
+                    model=model, 
+                    tokenizer=tokenizer, 
+                    max_new_tokens=100, 
+                    temperature=temperature, 
+                    return_full_text=False, 
+                    do_sample=True,
+                    num_beams=3,
+                    )
+    hf = HuggingFacePipeline(pipeline=pipe)
+    chain = LLMChain(llm=hf, prompt=chat_prompt_template)
+    return chain
+
+def generate(
+    model_name: str,
+    template: str,
+    input_values: dict[str, str],
+    output_parser: BaseOutputParser[OutputType],
+    temperature: float = 0.7,
+) -> OutputType:
+    input_variables = re.findall(r"{(.*?)}", template)
+    assert (
+        set(input_variables) == set(list(input_values.keys()) + ["format_instructions"])
+        or set(input_variables) == set(list(input_values.keys()))
+    ), f"The variables in the template must match input_values except for format_instructions. Got {sorted(input_values.keys())}, expect {sorted(input_variables)}"
+    # process template
+    template = format_docstring(template)
+    chain = obtain_chain(model_name, template, input_variables, temperature)
+    if "format_instructions" not in input_values:
+        input_values["format_instructions"] = output_parser.get_format_instructions()
+    result = chain.predict([logging_handler], **input_values)
+    prompt = logging_handler.retrive_prompt()
+    print(f"Prompt:\n {prompt}")
+    print(f"Result:\n {result}")
+    try:
+        parsed_result = output_parser.parse(result)
+    except KeyboardInterrupt:
+        raise KeyboardInterrupt
+    except Exception as e:
+        log.debug(
+            f"[red] Failed to parse result: {result}\nEncounter Exception {e}\nstart to reparse",
+            extra={"markup": True},
+        )
+        reformat_parsed_result = format_bad_output(
+            result, format_instructions=output_parser.get_format_instructions()
+        )
+        print(f"Reformatted result:\n {reformat_parsed_result}")
+        parsed_result = output_parser.parse(reformat_parsed_result)
+    log.info(f"Generated result: {parsed_result}")
+    return parsed_result
+
+def format_bad_output(
+    ill_formed_output: str,
+    format_instructions: str,
+    model_name: str = "gpt-3.5-turbo",
+) -> str:
+    template = """
+    Given the string that can not be parsed by json parser, reformat it to a string that can be parsed by json parser.
+    Original string: {ill_formed_output}
+
+    Format instructions: {format_instructions}
+
+    Please only generate the JSON:
+    """
+    chain = obtain_chain(
+        model_name=model_name,
+        template=template,
+        input_variables=re.findall(r"{(.*?)}", template),
+    )
+    input_values = {
+        "ill_formed_output": ill_formed_output,
+        "format_instructions": format_instructions,
+    }
+    reformat = chain.predict([logging_handler], **input_values)
+    log.info(f"Reformated output: {reformat}")
+    return reformat
+
+def obtain_chain(
+    model_name: str,
+    template: str,
+    input_variables: list[str],
+    temperature: float = 0.7,
+    max_retries: int = 6,
+) -> LLMChain:
+    """
+    Using langchain to sample profiles for participants
+    """
+    if model_name in ["cmu-lti/sotopia-pi-mistral-7b-BC_SR", "cmu-lti/sotopia-pi-mistral-7b-BC_SR_4bit", "mistralai/Mistral-7B-Instruct-v0.1"]:
+        return obtain_chain_hf(
+            model_name=model_name,
+            template=template,
+            input_variables=input_variables,
+            temperature=temperature,
+            max_retries=max_retries,
+        )
+    
+    model_name = _return_fixed_model_version(model_name)
+    chat = ChatLiteLLM(
+        model=model_name,
+        temperature=temperature,
+        max_tokens=2700,  # tweak as needed
+        max_retries=max_retries,
+    )
+    human_message_prompt = HumanMessagePromptTemplate(
+        prompt=PromptTemplate(template=template, input_variables=input_variables)
+    )
+    chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])
+    chain = LLMChain(llm=chat, prompt=chat_prompt_template)
+    return chain
+
+def _return_fixed_model_version(model_name: str) -> str:
+    model_version_map = {
+        "gpt-3.5-turbo": "gpt-3.5-turbo-0613",
+        "gpt-3.5-turbo-finetuned": "ft:gpt-3.5-turbo-0613:academicscmu::8nY2zgdt",
+        "gpt-3.5-turbo-ft-MF": "ft:gpt-3.5-turbo-0613:academicscmu::8nuER4bO",
+        "gpt-4": "gpt-4-0613",
+        "gpt-4-turbo": "gpt-4-1106-preview",
+    }
+    return model_version_map[model_name] if model_name in model_version_map else model_name

sotopia_space/_header.md

@@ -0,0 +1,4 @@
+<br/>
+
+# Sotopia Space: A Huggingface Space for the Sotopia projects
+[⚙️ GitHub](https://github.com/sotopia-lab) | [🤗 HuggingFace](https://huggingface.co/collections/cmu-lti/sotopia-65f312c1bd04a8c4a9225e5b) | [💬 Discussions](https://github.com/orgs/sotopia-lab/discussions)

sotopia_space/benchmark.py

@@ -0,0 +1,71 @@
+import gradio as gr # type: ignore
+import pandas as pd
+from sotopia_space.constants import MODEL_OPTIONS
+# from sotopia_space.utils import apply_length_penalty 
+
+LP_MODE = "v2"
+original_df, ablation_df = None, None
+LP_original_dfs = {} 
+DEFAULT_LP = 0.5 
+
+available_models = [] # to be filled in later
+original_df, ablation_df = None, None
+
+# def slider_change_main(length_penalty):
+#     global original_df, ablation_df, LP_MODE
+#     adjusted_df = apply_length_penalty(original_df, ablation_df, length_penalty, mode=LP_MODE, LP_original_dfs=LP_original_dfs) 
+#     adjusted_df = adjusted_df[["Model", "Overall Elo", "Task-Avg Elo", "# battles", "Length"]]
+#     adjusted_df = adjusted_df.sort_values(by="Overall Elo", ascending=False)
+#     # adjusted_df = add_winrates(adjusted_df, LP=length_penalty) 
+#     # adjusted_df = adjusted_df.drop(columns=["Length"])
+#     adjusted_df.insert(0, "Rank", range(1, 1 + len(adjusted_df)))
+#     return adjusted_df
+
+# def slider_change_full(length_penalty, show_winrate):
+#     global original_df, ablation_df, LP_MODE
+#     adjusted_df = apply_length_penalty(original_df, ablation_df, length_penalty, mode=LP_MODE, LP_original_dfs=LP_original_dfs)
+#     # sort the model by the "Task-Avg Elo" column
+#     adjusted_df = adjusted_df.sort_values(by="Overall Elo", ascending=False)
+#     adjusted_df.drop(columns=["Overall Elo", "Task-Avg Elo", "# battles", "Length"], inplace=True)
+#     if show_winrate == "none":
+#         adjusted_df.insert(0, "Rank", range(1, 1 + len(adjusted_df)))
+#         return adjusted_df
+#     elif show_winrate == "gpt-3.5":
+#         adjusted_df = add_winrates_tasks(adjusted_df, ref="gpt-3.5", LP=length_penalty)
+#     elif show_winrate == "gpt-4":
+#         adjusted_df = add_winrates_tasks(adjusted_df, ref="gpt-4", LP=length_penalty)
+#     adjusted_df.insert(0, "Rank", range(1, 1 + len(adjusted_df)))
+#     return adjusted_df
+
+def benchmark_table():
+    global original_df, ablation_df
+    global LP_original_dfs, LP_MODE
+    
+    gr.Markdown(f"**Version**: sotopia (v1.01; 2024.04.22) | **# Examples**: 7200 | **# Models**: {len(MODEL_OPTIONS)} | **# Comparisons**: x", elem_classes="markdown-text")
+                
+    with gr.TabItem("BARTScore on 100 samples", elem_id="od-benchmark-tab-table-ablation", id=0, elem_classes="subtab"):
+        # original_df, ablation_df = skip_empty_original_df, skip_empty_ablation_df
+        original_df = pd.read_json('data_dir/bartscore_100_results.jsonl', lines=True)
+        # default_main_df = apply_length_penalty(original_df, ablation_df, length_penalty=DEFAULT_LP, mode=LP_MODE, LP_original_dfs=LP_original_dfs) 
+        # original_df = original_df.sort_values(by="GOAL [0, 10]", ascending=False)
+        # add a Rank column to the first columnn (starting from 1)
+        # original_df.insert(0, "Rank", range(1, 1 + len(original_df)))
+        with gr.Row():
+            with gr.Column(scale=4):
+                gr.Markdown("**Vs GPT3.5**: Models are evaluated using BARTScore") 
+            # with gr.Column(scale=1):
+                # length_penlty_slider = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=DEFAULT_LP, label="Length Penalty", elem_id="length-penalty-slider") 
+        # checkbox_skip_empty = gr.Checkbox(label="Skip empty results", value=False, elem_id="skip-empty-checkbox", scale=2)
+        TYPES = ["number", "markdown", "number"]
+        leaderboard_table = gr.components.Dataframe(
+            value=original_df,
+            datatype=TYPES,
+            # max_rows=None,
+            height=1000,
+            elem_id="leaderboard-table",
+            interactive=False,
+            visible=True,
+            min_width=60,
+            )
+        # return leaderboard_table
+        #length_penlty_slider.change(fn=slider_change_main, inputs=[length_penlty_slider], outputs=[leaderboard_table])

sotopia_space/chat.py

@@ -0,0 +1,284 @@
+import os
+import gradio as gr # type: ignore
+# Functions for creating the chat interface
+from functools import cache
+from typing import Literal
+import json
+from collections import defaultdict
+from utils import Environment, Agent, get_context_prompt, dialogue_history_prompt
+from sotopia_pi_generate import prepare_model, generate_action
+from sotopia_space.constants import MODEL_OPTIONS
+
+DEPLOYED = os.getenv("DEPLOYED", "true").lower() == "true"
+DEFAULT_MODEL_SELECTION = "gpt-3.5-turbo"
+TEMPERATURE = 0.7
+TOP_P = 1
+MAX_TOKENS = 1024
+
+ENVIRONMENT_PROFILES = "profiles/environment_profiles.jsonl"
+AGENT_PROFILES = "profiles/agent_profiles.jsonl"
+RELATIONSHIP_PROFILES = "profiles/relationship_profiles.jsonl"
+
+Action = Literal['none', 'action', 'non-verbal communication', 'speak', 'leave']
+ACTION_TYPES: list[Action] = ['none', 'action', 'non-verbal communication', 'speak', 'leave']
+
+
+
+@cache
+def get_sotopia_profiles(env_file=ENVIRONMENT_PROFILES, agent_file=AGENT_PROFILES, relationship_file=RELATIONSHIP_PROFILES):
+    with open(env_file, 'r') as f:
+        data = [json.loads(line) for line in f.readlines()]
+    
+    code_names_count = defaultdict(int)
+    environments = []
+    environment_dict = {}
+    for profile in sorted(data, key=lambda x: x['codename']):
+        env_obj = Environment(profile)
+        if profile['codename'] in code_names_count:
+            environments.append((
+                "{}_{:05d}".format(profile['codename'], 
+                                   code_names_count[profile['codename']]
+                                   ), 
+                env_obj._id
+                ))
+        else:
+            environments.append((profile['codename'], env_obj._id))
+        environment_dict[env_obj._id] = env_obj
+        code_names_count[profile['codename']] += 1
+    
+    with open(agent_file, 'r') as f:
+        data = [json.loads(line) for line in f.readlines()]
+    
+    agent_dict = {}
+    for profile in data:
+        agent_obj = Agent(profile)
+        agent_dict[agent_obj._id] = agent_obj
+        
+    with open(relationship_file, 'r') as f:
+        data = [json.loads(line) for line in f.readlines()]
+    
+    relationship_dict = defaultdict(lambda : defaultdict(list))
+    for profile in data:
+        relationship_dict[profile['relationship']][profile['agent1_id']].append(profile['agent2_id'])
+        relationship_dict[profile['relationship']][profile['agent2_id']].append(profile['agent1_id'])
+    
+    return environments, environment_dict, agent_dict, relationship_dict
+
+def chat_introduction():
+    with gr.Column(scale=2):
+        gr.Image(
+            "images/sotopia.jpg", elem_id="banner-image", show_label=False
+        )
+    with gr.Column(scale=5):
+        gr.Markdown(
+            """# Sotopia Space
+            **Chat with different social agent models including [sotopia-pi](https://github.com/sotopia-lab/sotopia-pi), GPT and so on in sotopia space!**
+
+            ➡️️ **Intended Use**: Sotopia space is intended to showcase the social intelligence ability of different social agents in interesting social scenarios. 
+            
+            ✨ **Guidance**: 
+            
+            Step (1) Select a social scenario that interests you in "Scenario Selection"
+            
+            Step (2) Select a social agent you want to chat with in "Model Selection"
+
+            Step (3) Select which character you and your social agent will play in the scenario in "User Agent Selection" and "Bot Agent Selection" 
+            
+            Step (4) Negotiate/debate/cooperate with the social agent to see whether your goal or their social goal can be achieved.
+
+            ⚠️ **Limitations**: The social agent can and will produce factually incorrect information, hallucinating facts and potentially offensive actions. It can produce problematic outputs, especially if prompted to do so.
+
+            🗄️ **Disclaimer**: User prompts and generated replies from the model may be collected solely for the purpose of pure academic research. By using this demo, users implicitly agree to these terms.
+            """
+        )
+    # with gr.Column(scale=1):
+    #     toggle_dark = gr.Button(value="Toggle Dark")
+
+def create_user_agent_dropdown(environment_id):
+    _, environment_dict, agent_dict, relationship_dict = get_sotopia_profiles()
+    environment = environment_dict[environment_id]
+    
+    user_agents_list = []
+    unique_agent_ids = set()
+    for x, _ in relationship_dict[environment.relationship].items():
+        unique_agent_ids.add(x)
+    
+    for agent_id in unique_agent_ids:
+        user_agents_list.append((agent_dict[agent_id].name, agent_id))
+    return gr.Dropdown(choices=user_agents_list, value=user_agents_list[0][1] if user_agents_list else None, label="User Agent Selection")
+
+def create_bot_agent_dropdown(environment_id, user_agent_id):
+    _, environment_dict, agent_dict, relationship_dict = get_sotopia_profiles()
+    environment, user_agent = environment_dict[environment_id], agent_dict[user_agent_id]
+    
+    bot_agent_list = []
+    for neighbor_id in relationship_dict[environment.relationship][user_agent.agent_id]:
+        bot_agent_list.append((agent_dict[neighbor_id].name, neighbor_id))
+        
+    return gr.Dropdown(choices=bot_agent_list, value=bot_agent_list[0][1] if bot_agent_list else None,  label="Bot Agent Selection")
+
+def create_environment_info(environment_dropdown):
+    _, environment_dict, _, _ = get_sotopia_profiles()
+    environment = environment_dict[environment_dropdown]
+    text = environment.scenario
+    return gr.Textbox(label="Scenario", lines=1, value=text)
+
+def create_user_info(user_agent_dropdown):
+    _, _, agent_dict, _ = get_sotopia_profiles()
+    user_agent = agent_dict[user_agent_dropdown]
+    text = f"{user_agent.background} {user_agent.personality}"
+    return gr.Textbox(label="User Agent Profile", lines=4, value=text)
+
+def create_bot_info(bot_agent_dropdown):
+    _, _, agent_dict, _ = get_sotopia_profiles()
+    bot_agent = agent_dict[bot_agent_dropdown]
+    text = f"{bot_agent.background} {bot_agent.personality}"
+    return gr.Textbox(label="Bot Agent Profile", lines=4, value=text)
+
+def create_user_goal(environment_dropdown):
+    _, environment_dict, _, _ = get_sotopia_profiles()
+    text = environment_dict[environment_dropdown].agent_goals[0]
+    text = text.replace('(', '').replace(')', '')
+    if "<extra_info>" in text:
+        text = text.replace("<extra_info>", "\n\n")
+        text = text.replace("</extra_info>", "\n")
+    if "<strategy_hint>" in text:
+        text = text.replace("<strategy_hint>", "\n\n")
+        text = text.replace("</strategy_hint>", "\n")
+    return gr.Textbox(label="User Agent Goal", lines=4, value=text)
+
+def create_bot_goal(environment_dropdown):
+    _, environment_dict, _, _ = get_sotopia_profiles()
+    text = environment_dict[environment_dropdown].agent_goals[1]
+    text = text.replace('(', '').replace(')', '')
+    if "<extra_info>" in text:
+        text = text.replace("<extra_info>", "\n\n")
+        text = text.replace("</extra_info>", "\n")
+    if "<strategy_hint>" in text:
+        text = text.replace("<strategy_hint>", "\n\n")
+        text = text.replace("</strategy_hint>", "\n")
+    return gr.Textbox(label="Bot Agent Goal", lines=4, value=text)
+
+def sotopia_info_accordion(accordion_visible=True):
+    environments, _, _, _ = get_sotopia_profiles()
+    
+    with gr.Accordion("Create your sotopia space!", open=accordion_visible):
+        with gr.Row():
+            environment_dropdown = gr.Dropdown(
+                choices=environments,
+                label="Scenario Selection",
+                value=environments[0][1] if environments else None,
+                interactive=True,
+            )
+            model_name_dropdown = gr.Dropdown(
+                choices=MODEL_OPTIONS,
+                value=DEFAULT_MODEL_SELECTION,
+                interactive=True,
+                label="Model Selection"
+            )
+
+        with gr.Row():
+            user_agent_dropdown = create_user_agent_dropdown(environment_dropdown.value)
+            bot_agent_dropdown = create_bot_agent_dropdown(environment_dropdown.value, user_agent_dropdown.value)
+
+    with gr.Accordion("Check your social task!", open=accordion_visible):
+
+        scenario_info_display = create_environment_info(environment_dropdown.value)
+            
+        with gr.Row():
+            bot_goal_display = create_bot_goal(environment_dropdown.value)
+            user_goal_display = create_user_goal(environment_dropdown.value)
+            
+
+        
+        with gr.Row():
+            bot_agent_info_display = create_bot_info(bot_agent_dropdown.value)
+            user_agent_info_display = create_user_info(user_agent_dropdown.value)
+
+    # Update user dropdown when scenario changes
+    environment_dropdown.change(fn=create_user_agent_dropdown, inputs=[environment_dropdown], outputs=[user_agent_dropdown])
+    # Update bot dropdown when user or scenario changes
+    user_agent_dropdown.change(fn=create_bot_agent_dropdown, inputs=[environment_dropdown, user_agent_dropdown], outputs=[bot_agent_dropdown])
+    # Update scenario information when scenario changes
+    environment_dropdown.change(fn=create_environment_info, inputs=[environment_dropdown], outputs=[scenario_info_display])
+    # Update user agent profile when user changes
+    user_agent_dropdown.change(fn=create_user_info, inputs=[user_agent_dropdown], outputs=[user_agent_info_display])
+    # Update bot agent profile when bot changes
+    bot_agent_dropdown.change(fn=create_bot_info, inputs=[bot_agent_dropdown], outputs=[bot_agent_info_display])
+    # Update user goal when scenario changes
+    environment_dropdown.change(fn=create_user_goal, inputs=[environment_dropdown], outputs=[user_goal_display])
+    # Update bot goal when scenario changes
+    environment_dropdown.change(fn=create_bot_goal, inputs=[environment_dropdown], outputs=[bot_goal_display])
+
+    return model_name_dropdown, environment_dropdown, user_agent_dropdown, bot_agent_dropdown
+
+def instructions_accordion(instructions, according_visible=False):
+    with gr.Accordion("Instructions", open=False, visible=according_visible):
+        instructions = gr.Textbox(
+            lines=10,
+            value=instructions,
+            interactive=False,
+            placeholder="Instructions",
+            show_label=False,
+            max_lines=10,
+            visible=False,
+        )
+    return instructions
+
+def chat_tab():
+   # history are input output pairs
+    _, environment_dict, agent_dict, _ = get_sotopia_profiles()
+    def run_chat(
+        message,
+        history,
+        environment_selection,
+        user_agent_dropdown,
+        bot_agent_dropdown,
+        model_selection:str
+    ):
+        environment = environment_dict[environment_selection]
+        user_agent = agent_dict[user_agent_dropdown]
+        bot_agent = agent_dict[bot_agent_dropdown]
+        
+        context = get_context_prompt(bot_agent, user_agent, environment)
+        dialogue_history, next_turn_idx = dialogue_history_prompt(message, history, user_agent, bot_agent)
+        prompt_history = f"{context}{dialogue_history}"
+        agent_action = generate_action(model_selection, prompt_history, next_turn_idx, ACTION_TYPES, bot_agent.name, TEMPERATURE)
+        return agent_action.to_natural_language()
+    
+    with gr.Column():
+        with gr.Blocks():
+            model_name_dropdown, scenario_dropdown, user_agent_dropdown, bot_agent_dropdown = sotopia_info_accordion()
+            
+        with gr.Column():
+            with gr.Accordion("Start the conversation to achieve your goal!", open=True):
+                gr.ChatInterface(
+                    fn=run_chat,
+                    chatbot=gr.Chatbot(
+                        height=620,
+                        render=False,
+                        show_label=False,
+                        rtl=False,
+                        avatar_images=(
+                            "images/profile1.jpg",
+                            "images/profile2.jpg",
+                        ),
+                    ),
+                    textbox=gr.Textbox(
+                        placeholder="Write your message here...",
+                        render=False,
+                        scale=7,
+                        rtl=False,
+                    ),
+                    additional_inputs=[
+                        scenario_dropdown,
+                        user_agent_dropdown,
+                        bot_agent_dropdown,
+                        model_name_dropdown,
+                    ],
+                    submit_btn="Send",
+                    stop_btn="Stop",
+                    retry_btn="🔄 Retry",
+                    undo_btn="↩️ Delete",
+                    clear_btn="🗑️ Clear",
+                )

sotopia_space/constants.py

@@ -0,0 +1,39 @@
+MODEL_OPTIONS = [
+    "gpt-3.5-turbo",
+    "gpt-4",
+    "gpt-4-turbo",
+    "cmu-lti/sotopia-pi-mistral-7b-BC_SR",
+    "cmu-lti/sotopia-pi-mistral-7b-BC_SR_4bit",
+    "mistralai/Mistral-7B-Instruct-v0.1"
+    # "mistralai/Mixtral-8x7B-Instruct-v0.1",
+    # "togethercomputer/llama-2-7b-chat",
+    # "togethercomputer/llama-2-70b-chat",
+    # "togethercomputer/mpt-30b-chat",
+    # "together_ai/togethercomputer/llama-2-7b-chat",
+    # "together_ai/togethercomputer/falcon-7b-instruct",
+]
+
+MODEL_INFO = {
+    "Llama-2-13b-chat-hf.nosp": {"pretty_name": "Llama-2-13B-chat", "hf_model_id": "meta-llama/Llama-2-13b-chat-hf"},
+    "Llama-2-70b-chat-hf.nosp": {"pretty_name": "Llama-2-70B-chat", "hf_model_id": "meta-llama/Llama-2-70b-chat-hf"},
+    "Llama-2-7b-chat-hf.nosp": {"pretty_name": "Llama-2-7B-chat", "hf_model_id": "meta-llama/Llama-2-7b-chat-hf"},
+    "Llama-2-7b-chat-hf": {"pretty_name": "Llama-2-7B-chat (+sys prmpt)", "hf_model_id": "meta-llama/Llama-2-7b-chat-hf"},
+    "Mistral-7B-Instruct-v0.1": {"pretty_name": "Mistral-7B-Instruct", "hf_model_id": "mistralai/Mistral-7B-Instruct-v0.1"},
+    "Mistral-7B-Instruct-v0.2": {"pretty_name": "Mistral-7B-Instruct (v0.2)", "hf_model_id": "mistralai/Mistral-7B-Instruct-v0.2"},
+    "Mixtral-8x7B-Instruct-v0.1": {"pretty_name": "Mixtral-8x7B-Instruct", "hf_model_id": "mistralai/Mixtral-8x7B-Instruct-v0.1"},
+    "Nous-Hermes-2-Mixtral-8x7B-DPO": {"pretty_name": "Nous-Hermes-2-Mixtral-8x7B-DPO", "hf_model_id": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO"},
+    "Yi-34B-Chat": {"pretty_name": "Yi-34B-Chat", "hf_model_id": "01-ai/Yi-34B"},
+    "gemini-1.0-pro": {"pretty_name": "gemini-1.0-pro", "hf_model_id": "https://blog.google/technology/ai/google-gemini-ai/"},
+    "gemma-7b-it": {"pretty_name": "Gemma-7B-it", "hf_model_id": "google/gemma-7b"},
+    "gpt-3.5-turbo-0125": {"pretty_name": "gpt-3.5-turbo-0125", "hf_model_id": "https://platform.openai.com/"},
+    "gpt-4-0125-preview": {"pretty_name": "gpt-4-0125-preview", "hf_model_id": "https://platform.openai.com/"},
+    "tulu-2-dpo-70b": {"pretty_name": "Tulu-2-dpo-70b", "hf_model_id": "cmu-lti/tulu-2-dpo-70b"},
+    "vicuna-13b-v1.5": {"pretty_name": "Vicuna-13b-v1.5", "hf_model_id": "lmsys/vicuna-13b-v1.5"},
+    "zephyr-7b-beta": {"pretty_name": "Zephyr-7b-beta", "hf_model_id": "HuggingFaceH4/zephyr-7b-beta"},
+    "mistral-large-2402": {"pretty_name": "Mistral-Large", "hf_model_id": "https://mistral.ai/news/mistral-large/"},
+    "claude-3-opus-20240229": {"pretty_name": "Claude 3 Opus", "hf_model_id": "https://www.anthropic.com/claude"},
+    "claude-3-sonnet-20240229": {"pretty_name": "Claude 3 Sonnet", "hf_model_id": "https://www.anthropic.com/claude"},
+    "zephyr-7b-gemma-v0.1": {"pretty_name": "Zephyr-7b-Gemma", "hf_model_id": "HuggingFaceH4/zephyr-7b-gemma-v0.1"},
+    "Starling-LM-7B-beta": {"pretty_name": "StarlingLM-7B-beta", "hf_model_id": "Nexusflow/Starling-LM-7B-beta"},
+    "dbrx-instruct": {"pretty_name": "DBRX Instruct", "hf_model_id": "databricks/dbrx-instruct"}
+}

sotopia_space/utils.py

@@ -0,0 +1,223 @@
+from datasets import load_dataset, Dataset
+import os 
+import json 
+from datasets import load_dataset
+from datasets.utils.logging import disable_progress_bar # type: ignore
+from ui_constants import column_names, all_task_types
+import random 
+disable_progress_bar()
+import math 
+from sotopia_space.constants import MODEL_INFO
+
+id_to_data = None 
+model_len_info = None 
+
+
+def make_clickable_model(model_name):
+    global MODEL_INFO
+    if model_name in MODEL_INFO:
+        if MODEL_INFO[model_name]["hf_model_id"].startswith("http"):
+            link = MODEL_INFO[model_name]["hf_model_id"]
+            return f'🔒 <a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{MODEL_INFO[model_name]["pretty_name"]}</a>'
+        else:
+            link = f"https://huggingface.co/{MODEL_INFO[model_name]['hf_model_id']}"
+            return f'🔥 <a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{MODEL_INFO[model_name]["pretty_name"]}</a>'
+    else:
+        return model_name
+    
+
+def styled_error(error):
+    return f"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>"
+
+def styled_warning(warn):
+    return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>"
+
+def styled_message(message):
+    return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"
+        
+
+def estimated_win_rate(elo_a, elo_b, LP=0):
+    """
+    Calculate the estimated win rate for player A against player B using their Elo ratings.
+    :param elo_a: Elo rating of player A
+    :param elo_b: Elo rating of player B
+    :return: Estimated win rate for player A
+    """
+    exponent = (elo_b - elo_a)*(10**LP) / 400
+    probability_a_wins = 1 / (1 + 10 ** exponent)
+    return (1-probability_a_wins)*100
+
+
+
+# Formats the columns
+def formatter(x):
+    if type(x) is str:
+        x = x
+    else: 
+        x = round(x, 1)
+    return x
+
+
+def add_winrates(current_df, LP=0):
+    df = current_df.copy()
+    elo_column = "Task-Avg Elo" 
+
+    # Correct way to filter the DataFrame and get the Elo rating for "gpt-4-0125-preview"
+    model_a_elo = df[df["Model"].str.contains("gpt-4")][elo_column].iloc[0]
+
+    # Correct way to filter the DataFrame and get the Elo rating for "gpt-3.5-turbo-0125"
+    model_b_elo = df[df["Model"].str.contains("gpt-3.5")][elo_column].iloc[0]
+
+    
+    # Calculate the win rate of "gpt-4-0125-preview" against all models
+    df['Win% vs GPT-4'] = df[elo_column].apply(lambda x: estimated_win_rate(model_a_elo, x, LP=LP)).apply(formatter)    
+    df['Win% vs GPT-3.5T'] = df[elo_column].apply(lambda x: estimated_win_rate(model_b_elo, x, LP=LP)).apply(formatter)    
+    # apply the formatter for the two new columns 
+    cols = list(df.columns)
+    cols.remove("# battles"); cols.append("# battles")
+    cols.remove("Length"); cols.append("Length")
+    df = df[cols]
+    return df
+
+def add_winrates_tasks(current_df, ref="gpt-4", LP=0):
+    new_df = current_df.copy()
+    for t in all_task_types:
+        column = column_names[t]
+        model_a_elo = current_df[current_df["Model"].str.contains(ref)][column].iloc[0]
+        new_df[column] = current_df[column].apply(lambda x: estimated_win_rate(model_a_elo, x, LP=LP)).apply(formatter)
+    return new_df
+        
+
+def post_processing(df, model_len_info):
+    if model_len_info:
+        df["Length"] = df["model name "].apply(lambda x: model_len_info[x]["avg_len"])
+
+    for col in df.columns:
+        if col == "model name ":
+            df[col] = df[col].apply(lambda x: x.replace(x, make_clickable_model(x)))
+        else:
+            df[col] = df[col].apply(formatter) # For numerical values 
+    df.rename(columns=column_names, inplace=True)
+    df.sort_values(by="Task-Avg Elo", inplace=True, ascending=False)
+    # put the "Overall Elo" and "Task-Avg Elo" column to the front
+    # add the length info
+    df = df[["Model", "Task-Avg Elo"] + [col for col in df.columns if col not in ["Model", "Task-Avg Elo"]]]
+    return df
+
+def apply_length_penalty(original_df, ablation_df, length_penalty=0.2, mode='v1', LP_original_dfs=None):
+    """
+    Temporarily disable the length penalty feature
+    if mode == 'v2' and LP_original_dfs is not None:
+        L = f"{length_penalty:.1f}"
+        return LP_original_dfs[L]
+    original_df = original_df.copy()
+    ablation_df = ablation_df.copy()
+    # replace all values in original_df with the values as z = x - y * length_penalty where y is from ablation_df at the same row and column
+    # except for the "Model" column and the "# battles" column 
+    # do not assume the order of the rows are the same in both dataframes
+    for i, row in original_df.iterrows():
+        for col in original_df.columns:
+            if col == "Model" or col == "# battles" or col == "Length":
+                continue
+            # assert that the model names are the same in both dataframes
+            assert original_df.at[i, "Model"] == ablation_df[ablation_df["Model"] == row["Model"]]["Model"].values[0]
+            original_df[col] = original_df[col].astype(float)
+            if mode == "v1":
+                original_df.at[i, col] = original_df.at[i, col] - ablation_df[ablation_df["Model"] == row["Model"]][col].values[0] * length_penalty
+            elif mode == "v1.1":
+                diff = original_df.at[i, col] - ablation_df[ablation_df["Model"] == row["Model"]][col].values[0] 
+                original_df.at[i, col] = original_df.at[i, col] * (1-length_penalty) + diff*length_penalty
+    # post_processing
+    original_df = post_processing(original_df, model_len_info=None)
+    """
+    return original_df 
+
+def load_benchdata():
+    print("Loading sotopia data...")
+    bench_data = load_dataset("cmu-lti/sotopia", split="test")
+    return bench_data
+
+def load_benchdata_dict():
+    print("Loading sotopia data....")
+    bench_data = load_dataset("cmu-lti/sotopia", data_files="sotopia_episodes_v1_hf.jsonl")['train']
+    id_to_data = {}
+    for item in bench_data:
+        id_to_data[item["session_id"]] = item
+    return id_to_data
+
+def load_eval_results():
+    print("Loading sotopia Evaluation data...")
+    eval_results = load_dataset("WildEval/sotopia-Evaluation", "all", split="train")
+    return eval_results
+
+def load_infer_results(model_name):
+    print(f"Loading sotopia Results for {model_name}...")
+    infer_results = load_dataset("WildEval/sotopia-Results", model_name, split="train")
+    return infer_results
+
+def sample_an_eval_result(eval_results, model_list=[], tag_list=[]):
+    global id_to_data          
+    eval_results = list(eval_results)
+    random.shuffle(eval_results)
+    for eval_item in eval_results:  
+        # print(json.dumps(eval_item, indent=2))
+        # print(f"## Session ID: {eval_item['session_id']}")
+        # eval_item["eval_id"]
+        assignment = eval_item['assignment']
+        model_1, model_2 = eval_item['model_1'], eval_item['model_2']
+        model_A = model_1 if assignment['A'] == model_1 else model_2
+        model_B = model_2 if assignment['B'] == model_2 else model_1
+        if len(model_list) >= 2:
+            if model_A not in model_list or model_B not in model_list:
+                continue
+        elif len(model_list) == 1:
+            if model_A != model_list[0] and model_B != model_list[0]:
+                continue
+        else:
+            pass 
+        if tag_list:
+            if set(tag_list).isdisjoint(set(eval_item['tags'])):
+                continue
+        winner = eval_item['winner']
+        # print(f"## Model A: {model_A} | Model B: {model_B} | Winner: {winner}")
+        task_type = eval_item['tags'][0] # primary task type
+        chat_history = eval_item['history']
+        last_query = eval_item['last_query']
+        # print(f"## Task Type: {task_type}")
+        # print(f"## Chat History: {chat_history}")
+        # print(f"## Last Query -->  USER: {last_query}")
+
+        model_A_output = eval_item['model_1_output'] if model_1 == model_A else eval_item['model_2_output']
+        model_B_output = eval_item['model_2_output'] if model_2 == model_B else eval_item['model_1_output']
+
+        if len(model_A_output.strip()) == 0 or len(model_B_output.strip()) == 0:
+            continue
+
+        conversation_input = id_to_data[eval_item['session_id']]["conversation_input"]
+        # print(f"\n\n\n## Model A ({model_A}) Output ##\n{model_A_output}")
+        # print(f"\n\n\n## Model B ({model_B}) Output ##\n{model_B_output}")
+
+        # print(f"\n\n\n## Winner ##\n{winner}")
+        # print(f"\n\n\n## GPT-4 Judgement ##\n{eval_item['parsed_result']}")
+
+        result_dict = {
+            "session_id": eval_item['session_id'],
+            "model_A": model_A,
+            "model_B": model_B,
+            "winner": winner,
+            "intent": id_to_data[eval_item['session_id']]["intent"],
+            "task_type": task_type,
+            "all_tags": eval_item['tags'],
+            "chat_history": chat_history,
+            "last_query": last_query,
+            "conversation_input": conversation_input,
+            "model_A_output": model_A_output,
+            "model_B_output": model_B_output,
+            "reason": eval_item['parsed_result']["reason"],
+            "choice": eval_item['parsed_result']["choice"],
+            "checklist": id_to_data[eval_item['session_id']]["checklist"],
+        }
+        break 
+    return result_dict
+
+#id_to_data = load_benchdata_dict()

start_app.sh

@@ -0,0 +1,4 @@
+export OPENAI_API_KEY=$(cat openai_api.key)
+export HF_TOKEN=$(cat hf_token.key)
+
+gradio app.py

ui_constants.py

@@ -0,0 +1,191 @@
+from pathlib import Path
+
+DEFAULT_LP = 0.5
+
+banner_url = "https://github.com/sotopia-lab/sotopia-website/blob/main/public/bg_xl.png" # the same repo here.
+BANNER = f'<div style="display: flex; justify-content: flex-start;"><img src="{banner_url}" alt="Banner" style="width: 40vw; min-width: 300px; max-width: 800px;"> </div>'
+
+TITLE = "<html> <head> <style> h1 {text-align: center;} </style> </head> <body> <h1> 🦁 AI2 sotopia Leaderboard </b> </body> </html>"
+ 
+WINRATE_HEATMAP = "<div><img src='https://github.com/WildEval/sotopia-Leaderboard/blob/main/gradio/pairwise_win_fractions.png?raw=true' style='width:100%;'></div>"
+
+CITATION_TEXT = """@inproceedings{
+zhou2024sotopia,
+title={{SOTOPIA}: Interactive Evaluation for Social Intelligence in Language Agents},
+author={Xuhui Zhou and Hao Zhu and Leena Mathur and Ruohong Zhang and Haofei Yu and Zhengyang Qi and Louis-Philippe Morency and Yonatan Bisk and Daniel Fried and Graham Neubig and Maarten Sap},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024},
+url={https://openreview.net/forum?id=mM7VurbA4r}
+}
+"""
+
+
+column_names = {
+    "model name ": "Model",
+    "elo overall": "Overall Elo",
+    'Information seeking': 'InfoSek',
+    'Creative Writing': 'CrtWrt',
+    'Coding & Debugging': 'Code',
+    'Reasoning': 'Reason',
+    'Editing': 'Edit',
+    'Math': 'Math',
+    'Planning': 'Plan',
+    'Brainstorming': 'Brnstrm',
+    'Role playing': 'RolPly',
+    'Advice seeking': 'AdvSek',
+    'Data Analysis': 'DataAna',
+    'Others': 'Misc',
+    "average": "Task-Avg Elo",
+}
+
+all_task_types = [
+    'Information seeking',
+    'Creative Writing',
+    'Coding & Debugging',
+    'Reasoning',
+    'Editing',
+    'Math',
+    'Planning',
+    'Brainstorming',
+    'Role playing',
+    'Advice seeking',
+    'Data Analysis',
+    'Others'
+]
+
+
+
+js_light = """
+function refresh() {
+    const url = new URL(window.location);
+    if (url.searchParams.get('__theme') !== 'light') {
+        url.searchParams.set('__theme', 'light');
+        window.location.href = url.href;
+    }
+}
+"""
+
+js_code = """
+function scroll_top() {
+    console.log("Hello from Gradio!");  
+    const bubbles = document.querySelectorAll('.bubble-wrap');
+    bubbles.forEach((bubble, index) => {
+        setTimeout(() => {
+            bubble.scrollTop = 0;
+        }, index * 100); // Delay of 100ms between each iteration
+    });
+} 
+"""
+
+
+TASK_TYPE_STR = "**Tasks**: Info seeking (**InfoSek**), Creative Writing (**CrtWrt**), Coding&Debugging (**Code**), Reasoning (**Reason**), Editing (**Edit**), **Math**, Planning (**Plan**), Brainstorming (**Brnstrm**), Role playing (**RolPly**), Advice seeking (**AdvSek**), Data Analysis (**DataAna**)"
+
+css = """
+code {
+    font-size: large;
+}
+footer {visibility: hidden}
+.top-left-LP{
+    margin-top: 6px;
+    margin-left: 5px;
+}
+.markdown-text{font-size: 14pt}
+.markdown-text-small{font-size: 13pt}
+.markdown-text-tiny{font-size: 12pt}
+.markdown-text-tiny-red{
+    font-size: 12pt;
+    color: red;
+    background-color: yellow;
+    font-color: red;
+    font-weight: bold;
+}
+th {
+  text-align: center;
+  font-size: 17px; /* Adjust the font size as needed */
+}
+td {
+  font-size: 15px; /* Adjust the font size as needed */
+  text-align: center;
+}
+.sample_button{
+    border: 1px solid #000000;
+    border-radius: 5px;
+    padding: 5px;
+    font-size: 15pt;
+    font-weight: bold;
+    margin: 5px;
+}
+.chat-common{
+    height: auto;
+    max-height: 400px;
+    min-height: 100px; 
+}
+.chat-specific{
+    height: auto;
+    max-height: 600px;
+    min-height: 200px; 
+}
+#od-benchmark-tab-table-button{
+    font-size: 15pt;
+    font-weight: bold;
+} 
+.btn_boderline{
+    border: 1px solid #000000;
+    border-radius: 5px;
+    padding: 5px;
+    margin: 5px;
+    font-size: 15pt;
+    font-weight: bold; 
+}
+.btn_boderline_next{
+    border: 0.1px solid #000000;
+    border-radius: 5px;
+    padding: 5px;
+    margin: 5px;
+    font-size: 15pt;
+    font-weight: bold; 
+}
+.btn_boderline_gray{
+    border: 0.5px solid gray;
+    border-radius: 5px;
+    padding: 5px;
+    margin: 5px;
+    font-size: 15pt;
+    font-weight: italic; 
+}
+.btn_boderline_selected{
+    border: 2px solid purple;
+    background-color: #f2f2f2;
+    border-radius: 5px;
+    padding: 5px;
+    margin: 5px;
+    font-size: 15pt;
+    font-weight: bold;  
+}
+.accordion-label button span{
+    font-size: 14pt;
+    font-weight: bold;
+} 
+#select-models span{
+    font-size: 10pt;
+}
+#select-tasks span{
+    font-size: 10pt;
+}
+.markdown-text-details{
+    margin: 10px;
+    padding: 10px;
+}
+button.selected[role="tab"][aria-selected="true"] {
+    font-size: 18px; /* or any other size you prefer */
+    font-weight: bold;
+}
+#od-benchmark-tab-table-ablation-button {
+    font-size: larger; /* Adjust the font size as needed */
+}
+.plotly-plot{
+    height: auto;
+    max-height: 600px;
+    min-height: 600px; 
+}
+"""

utils.py

@@ -0,0 +1,55 @@
+from typing import List, Tuple
+import ast
+import re
+
+class Agent:
+    def __init__(self, agent_profile):
+        self._id = agent_profile["agent_id"]
+        
+        self.agent_profile = agent_profile
+        self.agent_id = agent_profile["agent_id"]
+        self.name = self.get_name(agent_profile)
+        self.background = self.get_background(agent_profile)
+        self.secret = agent_profile["secret"]
+        self.personality = agent_profile["personality_and_values"]
+        self.goal = ""
+        
+    def get_name(self, agent_profile):
+        return agent_profile["first_name"] + " " + agent_profile["last_name"]
+    
+    def get_background(self, agent_profile):
+        name = self.name
+        return f"{name} is a {agent_profile['age']}-year-old {agent_profile['gender'].lower()} {agent_profile['occupation']}. {agent_profile['public_info']}"
+    
+class Environment:
+    
+    def __init__(self, env_profile):
+        self._id = env_profile["env_id"]
+        
+        self.environment_profile = env_profile
+        self.codename = env_profile["codename"]
+        self.scenario = env_profile["scenario"]
+        self.agent_goals = env_profile["agent_goals"]
+        self.relationship = env_profile["relationship"]
+        
+        
+def get_context_prompt(machine_agent, human_agent, environment):
+    return f"Here is the context of this interaction:\n Scenario: {environment.scenario}\nParticipants: {human_agent.name} and {machine_agent.name}\n{human_agent.name}'s background: {human_agent.background} Personality and values description: {human_agent.personality} \n{machine_agent.name}'s background: {machine_agent.background} Personality and values description: {machine_agent.personality} {machine_agent.name}'s secrets: {machine_agent.secret}\n{human_agent.name}'s goal: Unknown\n{machine_agent.name}'s goal: {environment.agent_goals[1]}\nConversation Starts:"
+        
+def dialogue_history_prompt(message, history, user_agent, bot_agent):
+    dialogue_history = ""
+    for idx, turn in enumerate(history):
+        user_message, bot_message = turn
+        # TODOTODO (haofeiyu): we first assume that human talks first
+        user_turn_idx = idx * 2
+        bot_turn_idx = idx * 2 + 1
+        if not bot_message.startswith("["): # if action type == speak, need to add 'said: ' to be consistent with the dialog prompt
+            bot_message = 'said:"' + bot_message + '"'
+        dialogue_history = f"""{dialogue_history}\n\nTurn #{user_turn_idx} {user_agent.name} said: "{user_message}"\n\nTurn #{bot_turn_idx}: {bot_agent.name}: {bot_message}"""
+    curr_turn_idx = len(history) * 2
+    dialogue_history = f"""{dialogue_history}\n\nTurn #{curr_turn_idx} {user_agent.name} said: "{message}"\n"""
+    return dialogue_history, curr_turn_idx + 1
+
+def format_docstring(docstring: str) -> str:
+    """Format a docstring for use in a prompt template."""
+    return re.sub("\n +", "\n", docstring).strip()