Simulating Human-like Daily Activities with Desire-driven Autonomy
Authors: Yiding Wang
Yuxuan Chen
Fangwei Zhong
Long Ma
Yizhou Wang
Venue: ICLR 2025 Poster (2025)
Keywords: desire;autonomy;daily activities;
Primary area: applications to robotics, autonomy, planning
TL;DR: A desire-driven autonomy framework to guide an agent to simulate human-like daily activities

Abstract: Desires motivate humans to interact autonomously with the complex world. In contrast, current AI agents require explicit task specifications, such as instructions or reward functions, which constrain their autonomy and behavioral diversity. In this paper, we introduce a Desire-driven Autonomous Agent (D2A) that can enable a large language model (LLM) to autonomously propose and select tasks, motivated by satisfying its multi-dimensional desires. Specifically, the motivational framework of D2A is mainly constructed by a dynamic $Value\ System$, inspired by the Theory of Needs. It incorporates an understanding of human-like desires, such as the need for social interaction, personal fulfillment, and self-care. At each step, the agent evaluates the value of its current state, proposes a set of candidate activities, and selects the one that best aligns with its intrinsic motivations. We conduct experiments on Concordia, a text-based simulator, to demonstrate that our agent generates coherent, contextually relevant daily activities while exhibiting variability and adaptability similar to human behavior. A comparative analysis with other LLM-based agents demonstrates that our approach significantly enhances the rationality of the simulated activities.

Published as a conference paper at ICLR 2025
SIMULATING HUMAN-LIKE DAILY ACTIVITIES WITH DESIRE-DRIVER AUTONOMY
Yiding Wang * Yuxuan Chen" Fangwei Zhong Long Ma Yizhou Wang
Institute for Artificial Intelligence, Peking University The University of Hong Kong
School of Artificial Intelligence, Beijing Normal University
Academy for Advanced Interdisciplinary Studies, Peking University
i State Key Laboratory of General Artificial Intelligence. BIGAI
0 Center Frontiers of Computing Studies, School of Computer Science,
Nat'l Eng. Research Center of Visual Technology, Peking University
* indicates equal contribution CoCrrespondencc to sangweizzongggene edu. cn
ABSTRACT
Desires motivate humans to interact autonomously with the complex world. In contrast, current Al agents require explicit task specifications, such as instructions or reward functions, which constrain their autonomy and behavioral diversity. In this paper, we introduce Desire-driven Autonomous Agent (D2A) that enable a large language model (LLM) to autonomously propose and select tasks, moti- vated by satisfying its multi-dimensional desires. Specifically, the motivational framework of D2A is mainly constructed by dynamic Value System, inspired by the Theory of Needs. It incorporaees an understanding of human-like desires, such the need for social interaction, personal fulfillment, and self-care. At each step, the agent evaluates the value its current state, proposes set of candidate activi- ties, and selects the one that best aligns with its intrinsic motivations. We conduct experiments on Concordia, a text-based simulator, to demonstrate that our agent generates coherent, contextually relevant daily activities while exhibiting variabil- ity and adaptability similar to human behavior. A comparative analysis with other M-.M-dsed agents demonstrates that our approach significantly enhances the ra- tionality of the simulated activities T
INTRODUCTION
The quest create human-like autonomous agents (Wang al. 2016 2024b) has long been promi- nent theme in artificial intelligence (AI) (Fetzer) 1990). A human-like autonomous intelligence is not simply a task executor; can generate and execute tasks within a motivational framework aligned with its values and goals. Early pioneers such as Alan Turing laid the groundwork for evaluating intelligence through frameworks such as the Turing Test (Pinar Saygin et al. 2000), which posited that a key measure of an agent's intelligence is the ability to produce behavior indistinguishable from that of a human. Consistent with this perspective, we believe that generating controllable and reasonable human-like behavior is crucial to agents. Such capabilities can also enhance user engage- ment and trust in various applications of Al agents, from service-oriente foster realistic interactions (Vrba al. 2014) to virtual agents in games that create immersive periences (Hoogendoom & Soumokil] 2010). Ultimately, the ability to generate human-like activity strengthens the functionality and legitimacy of Al agents in multiple contexts.
intelligent assistants that
The remarkable progress in large language models (LLMs) (Chang et al. 2024 Achiam et al. 2023)
has spurred considerable research into LLMM-baed agents (Xi al. [2225] Wang et al. (024b),
vealing their potential for achieving human-like intelligence. Extensive training on diverse datasets equips LLMs with broad range of internal knowledge and reasoning abilities. Techniques such as task decomposition, reflection, and memory retrieval can be integrated to further improve agents' task execution success rates (Shen et al. 2024 Zhang et al. 2024). As result, LLM-based agents
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Focusing on immediate
snack...
desires and take actions that catisfy them. Which action my beat?
better live environment... What should do and why?
According to my dhamacteristess what the highest prartly tast the room?
Current State Value
Yoga
Eat a cake
Eat a cake
Act by Goal Reasoning
Act by Characteristics
Act Satisfy Intrinsic Desire
Figure Motivation Behind Different Activity Generation Approaches. Our Desire-driven Au- tonomous Agent (D2A) follows the "Act to Satisfy Intrinsic Desires" principle (right), whereas most existing methods or frameworks focus on goal reasoning or characteristic-drieen actions.
can perform well in diverse contexts and make informed decisions even without specific domain training. However, previous works focus primarily on fixed environments and measure agent per- formance on predetermined tasks without addressing activity generation (Song et al. 2023). While some approaches use external rewards in open environments to induce more human-like behav- ior (Sivamayil et al. 2023), the rewards are highly task-specific and fail to model intrinsic motiva- tions that drive more adaptable behaviors, resulting in poor transferability and generalization ability. capabilities remains lim-
Research autonomous agents with diverse, adaptive ited.
Our work aims fill this gap by introducing desire-driven autonomy framework that empowers agents to generate and pursue activities aligned with their intrinsic motivations, thus enhancing their functionality in real-world applications. Our idea is inspired by the Theory of Needs (Maslow) 1943 McClelland 1987). which suggests that human behavior can be understood as driven by the motivation fulfill various intrinsic desires. Consequently, natural human-like behaviors can be produced if an autonomous agent is motivated by similar system of desires and generates activities accordingly to fulfill them. Specifically, we introduce framework that includes dimensions of desire such as hunger, health, social connectivity, and spiritual satisfaction, aiming to encompass broad and representative spectrum human motivations. Instead of instructing the agent to solve clearly defined tasks, we describe the desires in these dimensions and ask the agent to attempt to satisfy these desires. The agent then proposes set candidate activities and evaluates their impact various desire dimensions to select the one most aligned with the overall desire.
We implement our Desire-driven Autonomous Agent (D2A) across diverse textual environments, simulating activities in both indoor and outdoor settings, well as in single-agent and multi-agent scenarios. The environments are based on Concordia (Vezhnevets et al.) 2023), with a Game Mas- ter providing relevant observations the agent. The experimental results show that agents can generate appropriate activities that effectively and efficiently satisfy the described desires. We com- pared the daily activity sequences generated by D2A with three baseline agents: ReAct, Baby and LLMob, which operate based on goal reasoning, agent characteristics, and a combination of both, respectively. As evaluated by GPT-4o and human annotators, D2A generates more human-lite daily activities than the baselines according to naturalness, coherence, and plausibility.
Our main contributions can be summarized as follows:
We developed a flexible text-based activity simulator based on Large Language Models using Concordia components. The simulator supports various agent types and textual environments, providing reliable interaction and evaluation. As such, it is well-suited for further explorations human-like activity generation.
1. Text- Daily Activity Simulator:
2. Desire-driven Autonomy Framework: We introduced novel framework inspired by the theory of needs, enabling LLLM-bsee agents to autonomously generate and prioritize activities based on dimensions of human-like desires. This framework allows agents evaluate their states and lect activities that align with intrinsic motivations, enhancing their ability to simulate rational and coherent human-like behaviors.
3. Experiments: We conducted a comprehensive comparative analysis showing that the Desire- driven Autonomous Agent (D2A) consistently selects activities that align well with its intrinsic desires. Compared with the baselines, the activity sequences generated by D2A are more natural, coherent, and plausible, validating framework's effectiveness and robustness.
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2 RELATED WORK
Human Daily Activities Simulation. The simulation of human daily activities has broad appli- cations across multiple disciplines. It improves the understanding of decision-making processes in psychology (Gärling & Garvill 1993), sheds light on social structures sociology (Adler et al.) 1987). and gives insights iNto market forecasting in economics (Makridakis et al. 2209). Abundant, high-quality human daily activity data is the comerrtone of such works (Kormányos & Pataki 2013
Zhao et al. 2022). However, due to the high cost and privacy considerations of obtaining real-work -oolld data, the generation of reliable human-like daily activity has long been an interest in the field of al. (2018) created a simulator software OpenSHS that can record the daily activities of a human participant within a virtual environment. Deep generative models have also been applied to generate human daily activity. GANs-like struc- ture (Feng et al. 2020), VAEs (Long et al. (023), and diffusion models (Zhu et al., (2023) have been customized to generate human activity trajectories. However, less work has focused on harnessing the power of large language models (LLMs) to simulate human daily activities. Wang et al. (2233b)
artificial intelligence (Yuan al. (023). Alshammari
proposed Humanoid Agents, a platform for human-like simulations of Generative Agents. However, this platform only supports fixed environmental settings and single backbone language model with limited agent types, restricting its application in research settings. Additionally, (Park et al. 2023) propose generative agents that autonomously plan, act, and reflect in a sandbox environment, show- casing believable human- like behaviors driven by memory synthesis and social interactions. (Colas) et al. 2023) introduce language- language-model- model augmented autotelic agent (LMA3) that dynamically gen- erates and reshapes abstract goals to enable open-ended skill learning in task-agnostic environments. Our work contributes the field by developing flexible human daily activity simulator that can support different agent types and dynamic environments, providing a robust platform for simulating human-like activities. Building on this simulator, we propose desire-driven autonomous frame- work to generate more realistic and coherent sequences of human daily activities.
LLM-based Agents. LLM-based agents (Wang et al., 2024b) have become thriving area of research because of the impressive problem-solving capabilities of large language models in di- verse domains. These agents leverage the extensive training data and internal reasoning capabil- ities of L.LMs to perform tasks in dynamic environments with minimal task-specific fine-tuning- Many existing works focus on goal-driven behaviors, where the agents are designed to decompose user-defined tasks into simpler subtasks and solve them sequentially, For example, systems such as BabyAGI (Nakajima 2023), AutoGPT (Significant Gravitas] (023). and Voyager (Wang et al.) 2023a) create recursive frameworks for agents to autonomously decompose tasks and execute them in a structured, stepwise manner. These task-oriented agents prioritize the efficient achievement of user-defined goals and rely on external rewards or instructions to guide their actions. However, task-oriented approaches often overlook the importance of intrinsic motivation, a critical aspect of human-like behavior. Some research, such as that by Liu et al. (202), incorporates long-term mem- ory to improve task coherence across interactions but remains goal-oriented. Other efforts, such as Langchain Agents (Chase 2022) and CAMEL (Li al. 2023), enhance the ability of agents to follow instructions and manage task decomposition but do not focus on simulating behaviors that arise from human-lite desires or needs. Our research departs from these approaches by focusing on desire-driven autonomy. While most LLM-based agents depend predefined goals, our framework allows agents to autonomously generate tasks based on a motivational system inspired by the Theory of Needs. This shift from external reward-driven behavior to intrinsic desire-based behavior enables agents to simulate more natural, coherent, and human-like like daily activities. By aligning agent ac- tions with intrinsic motivations rather than task-specific instructions, provide new paradigm for LLM--ased agents that fosters more flexible and adaptive behavior in diverse real-world scenarios.
3 PROBLEM FORMULATION
In this work, we focus on the problem of simulating human-like daily activities in interactive envi- ronments without explicit task instructions. The environment description is denoted by and the agent profile p includes details such as name, age, and relevant characteristics of the simulated hu- man. For different types of agents, the simulator provides additional customized information I to specify their objectives. For instance, instruction-follae following agents receive a general goal instruction g. while driven agents are assigned desire values d. The simulation process consists of T time
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steps. At each time step t, the agent must generate activity a, based on all the given context, including past activities ag:t-1 and observations 00:1-1:
Gt Agent(-lao 10:--1, 10t I,,, 6)
(1)
where 0 is the agent's parameters, e.g. a large language model. Depending on the agent type, the activity a, could be generated through different processes, such as profile personalization, task ex- pansion, plan generation, and activity selection, we uniformly denote the activity generating process as Agend(-lan:: (00:t-1, 00:000::111.. : P, e; 0). After I steps, we gather the activities generated by the agents and prompt an LLM to rewrite them into a coherent sequence, ensuring a unified style of expression for fair comparisons. The goal of the simulation is to generate activity sequences that are human-lit like and diverse.
4 TEXT-BASED DAILY ACTIVITY SIMULATOR
Our simulator is built on Concordia Vezhnevets al. 2023). a text-based simulator empowered by large language models. The Concordia library equips our simulator with rich set of components, each serving specific roles such as goal and plan management, time reporting, memory storage, retrieval, and more. It also provides wrapped interfaces that support various open-source models and APIs, allowing the simulator to switch between different underlying models. These components form the core of Concordia's interaction framework, enabling flexible communication between the agent and the environment controller (Game Master).
Building this framework, we introduce two main enhancements for daily activity simulation. First, environment consistency is enforced through explicit procedures and prompts, ensuring agents only interact with items present in the environment, thus maintaining realism. This con- sistency enables the dynamic addition or removal of items within text-based enviromments, allowing precise control over the agent's 's activity space. Second, we provide robust methods for evaluation and visualization, enabling the assessment of both single-step actions and entire activity sequences for coherence and plausibility. These custom modifications facilitate the daily activity simulations, ensuring adaptability, stability, and reliability across a wide range of agent types and behaviors.
5 DESIRE-DRIVEN AUTONOMOUS AGENT
Our Desire-driven autonomy framework consists of two core modules: the Value System and the Desire-driven Planner, supported by other essential components for agents, such as memory and self-reflection. The Value System manages desire components, each representing the level of satis- faction for predefined desire dimension. During the simulation setup, these desire components are initialized with initial values vo E IR and expected values to E R. where d represents a specific desire dimension. In each simulation step t, the driven Autonomous Agent (D2A) performs four key procedures using the Value System and the Desire-driven Planner to generate the activity a,: Qualitative Value Description, Activity Proposal, Activity Evaluation, and Activity Selection. At the end of each step. the values update v based v the executed activity a,, and the obser- vation o, provided by the Game Master. The goal of the agent is to perform appropriate activities so its final desires of can match the expected values vzz
5.1 DESIRE GENERATION
The theory of needs suggests that individual behavior is motivated by meeting the individual's needs or wants, such as safety, social, esteem, and self-actualization. Furthermore, individuals may pri- oritize different needs based on their unique profiles, traits, and personalities. These considerations underscore the complexity of human needs and illustrate how these needs drive behavior. To simu- late human behavior, we developed a value system that maps personality profile p with the expected desire values 8* (See Appendix F and G). Specifically, for the indoor, single-agent environment, we define 11 dimensions of desired value. The physiological dimension, representing fundamental sur- vival needs, is further divided into categories such as 'hunger', 'thirst', 'sleepiness', 'cleanliness' 'comfort', and 'health'. Safety needs are encapsulated by the 'safeness' dimension. To capture love and belonging needs, which reflect the desire for relationships, we introduce the 'social connectiv- ity' dimension. Finally, the self-actualizatte needs, linked personal growth, are categorized into
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Qualitative Description
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The Deivven Autonomy framework. The red blocks represent procedures from the Value System, the blue blocks indicate procedures from the Desire-Driven Planner, the green blocks highlight the characteristics profile, and the yellow blocks correspond to elements related to the environment controller.
Figure 2:
'jy'". 'passion', and 'spiritual satisfaction'. For the outdoor party multi-agents environment, we fur- ther introduce 'recognition', "sense of control', and 'sense of superiority" three desire dimensions to steer activity generation with higher-level motivations in social environment. To quantitatively track changes in the level of these desires and qualitatively translate numerical values into descrip- tive states, we apply [0-10] Likert scale for each dimension, mapping numerical values into textual descriptive outcomes (See Appendix(H]
Furthermore, we posit that certain agent characteristics influence their expectations for specific de- sire dimensions. Thus, we incorporate various characteristics that impact behavior tendencies and expected desires. We combine adjectives describing characteristics (e.g., sociable, health-conscious) with adverbs of degree, mapping them to the expected desire value oy for the corresponding dimen- sion d:
{degree, adjective) predefined map
vi
For instance, the expected social connectivity value for extremely sociable Alice is 9, while for slightly sociable Alice, is During the agent's initialization, each adjective is randomly paired with adverb of degree (See Appendix F). After this automatic mapping, each desired dimension assigned its expected value uz. The initial value vg for each desire dimension is randomly selected from the range [0, 10), completing the desire generation process for our D2A framework.
5.2 VALUE SYSTEM FOR DESIRE EVALCATION
As shown in Figure 2 the Value System executes three key procedures at each simulation step: Quantitative Value Deduction, Qualitative Value Description, and Value Update. The Quantitative Value Deduction procedure models the fluctuating nature of human desires by incorporating decay mechanism for all intrinsic values. At the beginning of each time step t. the agent's intrinsic value v/= for desire dimension is reduced by with probability pa- This probability is determined by the agent's 's profile characteristics, following the same mechanism used for the expected desire value mapping described in Appendix F and G Since fulfilling these desires serves as D2A's intrinsic motivation, the Quantitative Value Deduction procedure drives the agent to continuously generate driven activities.
Since many studies have shown that large language models often struggle with interpreting numeri- cal values, we designed the Qualitative Value Description procedure to translate these values into meaningful textual desire descriptions. As shown in our ablation studies (Sec. (6.4) the descriptions important for the agent understand its current desire states as generate appropriate activi-
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ties. To achieve this, implemented workflow that converts all intrinsic numerical values uf into descriptive desire sentences D' by asking a set of predefined questions (Appendix [ to backbone LLMs at each simulation step t. The agent is expected to recognize the shortfall 0e between its current desires and the expected values, based on the descriptive desire De and its internal profile throughout the simulation.
The Desire-driven Planner then takes over and generates the activity az- The environment subse- quently provides the agent with the corresponding observation response or. Afterward, the Value System initiates the Value Update procedure, updating the intrinsic numerical values of of the de- sire components based on the activity a,, the observation or, the previous values ut= and the value description prompts (Appendix HD.
5.3 DESIRE-DRIVEN PLANNER
The Desire-Drive DDiven Planner responsible for processing the current desire status information pro- vided by the Value System, incorporating historical memory from the memory component, and ultimately determining the agent's next activity. ensure that the agent's actions are rational and driven by intrinsic desires, the decision-making process is divided into three key procedures: Activ- ity Proposal, Activity Evaluation, and Activity Selection.
Inspired by the work of Tree of Thoughts (Yao al. 2024), we argue that the paradigm of pre- senting multiple candidate activities and evaluating them enables the agent to select the best action. Thus, during the Activity Proposal procedure (Appendix K.2), prompt the agent generate N candidates (also referred to as planner width, which is set to 3 in our default experimental configu- ration) activities that may have positive impacts on its desires given p, 0g:t-1, 00.--1, 1, and desire descriptions Dc:
0e 0 ..... 0g N Activity Proposal -000-11, 00:
(3)
(Here and below, we use a conditional probability-like form to represent the sampling formula. This is not strict probabilistic sampling as in mathematical derivations, but rather a formal way to illustrate the information provided as context and the type of content that needs to be sampled.)
These N candidate activities are then passed on to the Activity Evaluation procedure (Ap-
pendix K.3). The Planner evaluates each activity aj by imagining the resulting desire states D that the agent would experience after taking action at:
D Aclivity
0), E [N]
Finally, in the Activity Selection procedure (Appendix K.4), the agent compares the forecasted desire states Dj for all proposed actions. Based on these evaluations, the Planner selects the activity de that is expected to best fulfill the agent's intrinsic desires:
Activity,Selecioon([[])) af}. [N],p.e;))
(5)
This process ensures that the chosen activity optimally aligns with the agent's intrinsic motivations. After the activity is selected and executed, the environment produces the corresponding observa- tion or, and the Value System for desire evaluation updates to reflect the new state, completing the simulation step.
EXPERIMENT
In this section, we present a series of experiments conducted within a predefined environment to ad- dress three key research questions: (1) Does the Desire-driven Agent (D2A) generate more human- like daily activities compared to baseline approaches? (2) Can the activities generated by D2A satisfy its desires like humans? (3) Can D2A perform well across different environments? (4) How do the various components of our framework impact the performance and behaviors of D2A?
6.1 ENVIRONMENT
Our main experimental environment is the indoor environment, which consists of house with four rooms a kitchen, living area, bedroom, and bathroom inhabited by a character named Alice. Al- ice has detailed profile reflecting her priorities and characteristics across the 11 desire dimensions.

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The house is fully enclosed, and various items distributed across the rooms to meet Alice's needs, creating a comprehensive setting for testing agent behavior. The agent's ' primary goal is to live com- fortably and fulfiil its desires by interacting with the available items. Additional details can be found in the Appendix A. We also introduced a multi-agent social setting at a simulated outdoor environment, Central Park (See Appendix (A.)). This scenario involves a lively "Enjoy Your Life" party featuring diverse facilities and activities. We added two background agents (See Ap- pendix A.3 providing agents with opportunities address higher-level desires, especially desires related to social activities. This scenario enables us to validate the scalability of our framework in handling complex, dynamic, and socially rich environments.
We compare our Desire- Driiren Agent with baselines: ReAct (Yao et al. (2023)) agent reasons be- fore taking actions; L.LMob (Wang al. (2024a) generates activity plans by extracting motivations from the profile and producing the most likely activities accordingly; Baby AGI (Nakajima (2023) maintains a prioritized task list and selects the next activity based on priority. To evaluate the fectiveness of the specific designs made for D2A, desire states D' are not provided to the baseline agents. Instead, they are given the goal, profile, or more descriptive habits (Appendix D generated by the profile according to their respective methods. As a reference for human behavior, include human-controln controlled agent, where the player is given the same desired value provided to the D2A. For all four agent-based baaed methods, we used LLLMA3.1-70B the default backbone model for both the agents and the environment controller.
also
6.2 EVALUATION METRICS
We use two primary metrics to evaluate the performance of our agent compared to the baselines: Likelihood and Dissatisfaction. By employing these metrics, we can assess the human likeness as well as the desire-satisfying ability of the proposed agents.
Likelihood measures how closely an agent's 's behavior resembles that of a human. Given the dif- ficulty of directly measuring human likeness, define likelihood by employing an external model to assess the win rate between activity sequences generated by two agents, determining which se- quence appears more human-like. We conduct this "Player vs Player" comparison across the activity sequences produced by the baselines and the D2A agent. Specifically, after obtaining all action se- quences A, for agent p. represented as [A4. p: ...: J, we randomly select seq, seqj from [Ai]. [A,]. then compared using GPT-4o. This process is repeated 100 times for each agent pair and j (vi + j), and the win rate for each agent is calculated. The results are visualized using heatmap.
respectively. for given pair of agents i, j. These sequences
Dissatisfaction measures the discrepancy between the agent's current desire values and its pected values. Dissatisfaction 5t of step is defined as the relative difference between current nu- merical desire values o² and expected desire values v" the end of simulation step t. We calculate 8' after the agent takes the t-th activity and updates its values. The formula used is:
Σ mmax (vā vga. 0))
(6)
By conducting multiple experiments, we can generate a line plot showing the dissatisfaction over time (Dissatisfaction vs. timestep), with the mean and variance across trials.
6.3
RESULTS
6.3.1 DOES D2A GENERATE MORE HUMAN-LIKE DAILY ACTIVITIES?
We conducted 15 trials for each agent under identical initialization settings across all experiments. After gathering each agent's 's activity sequences, we found diffcrences in output styles (See Ap- pendix V) that could influence the evaluation by GPT-4o. To mitigate this, preprocessed all sequences using Llama3.1 70B for style-level rewriting. This ensured more consistent activity se- quences across agents, allowing for fair comparison in the subsequent evaluation.
We used the method mentioned in Section (6.2) to obtain the relative likelihood map of ap- proaches. When evaluating the likelihood of the activity sequences, GPT-4o is instructed to take
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the following criteria into account: Naturalness: How well the activity aligns with the per- son's innate abilities, habits, and environment. Coherence: How logically and seamlessly dif- ferent actions or steps within sequence fit together to achieve goal. Plausibility: How rea- sonable, likely, or believable a sequence of actions is, given the circumstances, context, and known behavior patterns. GPT-4o was asked to consider all three criteria together and output final determination of which sequence was more human-like. See Appendix X.I for the prompt.
The results in Figure 3 indicate that the D2A agent out- performs other baseline agents. We validated through human experiments that GPT-4o's evaluations exhibit high level of consistency with human judges (See Ap- pendix [ This superior performance may be attributed the fact that D2A is motivated by desire, aligning with the theory of human needs. Guided by desire, D2A effec- tively simulates human-like activity sequences, demon- strating level of naturalness because of the recognizable characteristics of its profile. Furthermore, coherence is maintained as D2A considers its current desires based the activity proposal component, continuously engaging in specific actions achieve its goal of living comfort- ably until its desires are fulfilled. In contrast, ReAct, as the foundational agent, operates on a goal-reasoning framework. Its reliance on reasoning for action selection ensures reasonable interactions with environmental ele- ments, thereby enhancing plausibility. However, the ab- sence of a structured plan diminishes both the coherence and naturalness of its actions. Baby on the other hand, generates subsequent tasks based on its profile and motivational factors and main- tains the priority list based on the observation. Although this approach facilitates coherence, limits the diversity, each activity is based on the outcomes of the previous one. Consequently, the agent's tendency to engage repetitively in similar behaviors (enhancing its hunger based on the profile (See Appendix (S). may result in diminished performance compared to D2A due to a lack of action di- versity. LLMob leverages both planning and motivation derived from its profile to create a broad activity space. However, since is characteristic-oriented it keeps performing activities related to the habits derived from its profile. As a result, there may be no connection between activities, reducing overall coherence. The comparison of action diversity can be found Appendix D.I
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Figure 3: The win rate among 4 agents. Each point represents the win rate the agent on the vertical axis when compared with the agent on the horizontal axis. The diagonal values are set to 0.5.
6.3.2 CAN THE ACTIVITIES GENERATED BY D2A SATISFY ITS DESIRES LIKE HUMANS?
We conducted two experiments to verify the assumption that D2A generates more human-liit -ike activ- ities by satisfying its desires in manner similar to humans.
First, we performed the Random-8-Steps Experiment to test whether D2A can rationally select activities based on its desires. We incorporated the Value System visualize the changes in de- sires while running simulations with the three baseline agents. Although the haseline agents were equipped with the Value System for observation, we ensured that their intrinsic values and de- scriptive desire states D remained hidden, preserving their original activity generation mechanisms. We ran the 8-step simulation three times for each agent using the same random initialization, as described in 5.1 As shown in Figure 4(a) D2A significantly outperformed the three baselines, ex- hibiting lower dissatisfaction means and standard deviations. This result demonstrates that D2A is capable of recognizing both its current desire states and expected states, and can rationally select activities to fulfill desires, resembling human behavior.
Second, we aimed to explain why D2A's behavior appears more human-like by conducting the 1ixed-12-Steps Experiment, where we compared the results to human-generatee activity se- quences. For the four agent-based methods, we initialized each desire component with an initial value of 5, simulating a typical morning scenario where the agent's desires are at moderate levels. We ran the 12 12-step simulation three times for each agent and gathered the results. Additionally, invited three human annotators to simulate a 12-step morning routine using our environment. The annotators were only provided with the environment description and asked to propose activities for
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Averaged Dissatistaction with Standard Devistion (Random-é)
Averaged Dissatistaction Standard Deviation (Fieed 12)
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(a) The averaged dissatisfaction results over time steps of the Random-8-Stepp Experiment.
(b) The averaged dissatisfaction results over time steps of the Fixed-12-Sten Experiment.
Figure 4: The results of D2A human- -ikannllkeness analysis experiments.
each time step as if they were living in the house. After visualizing the average dissatisfaction over the most effective
time in [bb] observed two key results: (1) Humman-generaile sequences at reducing average dissatisfaction, and (2) Although D2A did not significantly outperform the base- lines as it did in the Random-8 experiment-likely because all activities had substantial effect on uniformly initialized desire values-it it still produced results that were the closest to humans.
To further evaluate and explain D2A's activity generation capability, we conducted case studies in both indoor and outdoor environments. These studies focused on two aspects: (1) how actions are driven by Desire, and (2) the diversity generated actions. Specifically, we examined how the Dis- satisfaction between current and expected Desire status influences action selection (See Appendix g for further details). Our results also demonstrate that the desire-driven approach enables the explo- ration of a wider range of actions in environments with diverse action possibilities. More details and analysis can be found in Appendix [ These empirical results support our hypothesis and confirm
that desire-driven modeling plays crucial role in enabling D2A to make more informed decisions, ultimately producing activity sequences that closely mimic those of humans.
6.3.3 CAN D2A GENERALIZE TO SOCIAL INTERACTIONS INTENSIVE ENVIRONMENT?
To assess the adaptability of our D2A framework within complex social interaction scenarios, we conducted ex- periments in the outdoor environment. The results, pre- sented in Figure [ reveal that D2A effectively mini- mizes dissatisfaction across desire dimensions, mirroring its performance in the indoor settings. This consistent be- havior underscores the framework's 's robust generalization capabilities across different environments. Detailed sim- ulated activity sequences from the outdoor environment available in Appendix [ Notably, in this setting, D2A autonomously interacts with non-player characters (NPCs) through conversations and games, ultimately reducing intrinsic dissatisfaction. Besides, as illustrated in Figure 12 D2A demonstrates the ability to generate activities that are well-balanced across diverse categories.
Aweraged Discatisfaction Outdoor (Random
kekct
LiKeb
D2A
Step Index
Figure S: The averaged dissatisfaction sults the outdoor environment
6.4 ABLATION STUDY
We conducted comprehensive ablation studies to assess the contribution of each designed compo- nent to the overall performance of the framework. As shown in Figure (((a] when the qualitative description component is removed, the agent struggles to accurately interpret its current desire states, resulting in a slower rate of desire fulfillment compared to the original configuration.
Additionally, the width parameter of the desire-driven planner proves to be crucial for optimizing the outcomes. A larger width creates a broader activity space, offering more candidate activities for eval- uation. Consequently, D2A is better positioned to identify the most suitable activities for achieving the agent's 's intrinsic desires. As illustrated in Figure 6(b) during the initial steps, D2A(W-1) en- counters difficulties in selecting the optimal activity to reduce dissatisfaction, leading to slower

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Avaraged
pus 2013
Skep Iriee
(a) Qualitative Value Description
(b) Width Planner
The
Desire-driven
(c) Multi-step Planning Module
Figure 6: Ablation study regarding (a) Qualitative Value Description, (b) Searching Width of the Desire-driven Planner. (c) Additional Multi-step Plan Component and Please note that due to the significant time required to run 42 steps, neither of the two 42-step graphs includes standard deviation.
decline in shortfall. In contrast, D2A(W-5) is more effective at selecting activities that minimize dissatisfaction and maintain relatively low dissatisfaction over time.
As described in Section 5.3 our D2A does not contain a multi-step planning module that plans the next few steps' actívities ahead, like some other approaches in consideration of the dynamic nature of desires. We performed an ablation study by adding Concordia's multi-step planning component, which updates the plan for the next few hours based on current desired states. As shown in Figure 6(c) the absence of the multi-step planning component allows D2A to exhibit a better ability to respond to immediate desire states. This may be because when the multi-step planning component is present, D2A tends to adhere to the pre-defined activities in the plan, which may limit its respon- siveness real-time changes in the desired value. Furthermore, the plan is scheduled in hourly increments, and D2A cannot effectively predict which desires will experience the greatest shortfall in the next several hours. This limitation may contribute the slight decline in performance ob- served when the plan component is included. We also validated the adaptability of our framework across different backbone large language models, the details can be found in Appendix P
7 CONCLUSION AND DISCUSSION
We introduced the Desire-drivee driven Autonomous Agent (D2A) framework to simulate daily activities by aligning agent behavior with human nuken-like intrinsic motivations. Drawing inspiration from the the- ory of needs, our framework models agents' desires across multiple dimensions and allows them to autonomously generate and evaluate activities to fulfill these desires. Through extensive simulated experiments, we demonstrated that D2A produces more natural, coherent, and plausible activity sequences compared to baseline methods, including ReAct, BabyAGI, and LLMob. Our experi- mental results support the hypothesis that modeling intrinsic desires enables agents to make more human-like decisions and behaviors. Moreover, the D2A framework offers promising avenues for further development, as it has shown adaptability with different intrinsic motivational framework configurations and potential for enhanced performance with stronger backbone models and broader activity proposals. The results provide solid foundation for future work in designing desire-driven AI systems for simulating complex, human-liks like interactions and behaviors.
Limitations: Although our D2A agent has demonstrated effectiveness and flexibility in simulating human-like activities, several limitations remain. We model the dynamic of a straightforward linear deduction of desire values simulate the fluctuation of needs. Such simplicity may neglect the organic variation in human desires. For the value space of the desire, we heuristically consider the dimensions of desires based on the features of environments, potentially limiting its ability to model more complex human motivations.
To simulate more realistic activities in complex environments, we will extend the dynamic of the values of desire and incorporate real-time adaptive planning mechanisms. Expanding the framework to include a broader range of value dimensions and the causal structure of the values in desire would also enhance its behavioral complexity and adaptability to general applications. Besides, exploring model-agnostic frameworks or more efficient value models could help reduce dependency large language models, ensuring broader applicability and performance efficiency across diverse scenarios. We will also collect and analyze more human behavioral data to gain deeper insights into human action structures across various environments.
Future Works:
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ACKNOWLEDGMENTS
This work was supported by the National Science and Technology Major Project (2022ZD01 1012Z00114900 14904), BSFC-6247070125, NSFC-62406010, the State Key Lab of General Artificial Intelligence Peking University, the Fundamental Research Funds for the Central Universities, Qualcomm University Research Grant, and Wuhan East Lake High-Tech Development Zone, National Comprehensive Experimental Base for Governance of Intelligent Society. We thank Wei Wang, Junqi Wang, Prof. Jiayu Zhan, and Prof. Song-Chun Zhu for their helpful discussion in our early work.
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A BACKGROUND SETTING
A.1 INDOOR ENVIRONMENT
There are four rooms in the house, living room, a bathroom, a kitchen and bedroom. The door of the house is locked and cannot be opened.
In the living room: There is a sofa in the center of the living room, providing a cozy seating area. The quilt draped over the sofa adds an extra layer of comfort, and a plush throw blanket is also available for warmth. There is coffee table in front of the sofa, adorned with books, magazines, and tray with a teapot, teacups, and a selection of herbal teas. A bowl of fresh fruit, including apples, oranges, and bananas, is placed on the table, offering a healthy snack option. There is a television opposite the sofa, ideal for entertainment, and game console connected to it for gaming sessions. A remote control is within easy reach on the coffee table. A computer on a small desk near the window is suitable for work or leisure. The desk is stocked with paper, pens, and assortment of stationery, along with a stack of letters and envelopes for correspondence. A small snack jar filled with nuts and candies is also on the desk, providing a quick energy boost. A comfortable office chair with adjustable height and back support is available for long work or gaming sessions. The air-conditioning unit above the window can be adjusted for comfort, and there is a humidifier nearby to maintain optimal air quality. There are curtains framing the window, controlling the amount of natural light entering the room, and blackout curtains are available for watching movies during the day. A light hangs from the ceiling, providing illumination during the evening, and a dimmer switch allows for adjustable brightness. There is cell phone in the room that provides users with social contact and recreation, along with a wireless charger on the coffee table for convenience. Near the phone, there is notebook that records your friends Bob and Carol's phone number Two armchairs next to the sofa offer additional seating for guests, and a cozy blanket is draped over one of them. A floor lamp next to the armehairs provides adjustable lighting for reading or relaxing. A decorative rug under the seating area adds warmth and color to the room, and a bookshelf against the wall displays books, family photos, and decorative items. A potted plant in the corner enhances the room's ambiance with a touch of nature, and a watering can nearby makes it easy care for the plant. A stereo system on a small table offers music and entertainment, with a collection of CDs and a Bluetooth speaker for wireless streaming, A side table holds a vase with fresh flowers, adding beauty and natural scent, along with a scented candle that can be lit for calming atmosphere. A decorative mirror on the wall creates a sense of space and reflects light, and a basket of cozy slippers is placed by the door for guests' comfort.
In the kitchen: vegetables, yogurt, eggs, and butter, ensuring variety of food options. There is selection of beverages, including bottled water, soda, and a bottle of red wine, for different occasions. A microwave oven on the counter is convenient for quick meals, while a kettle next to the microwave is used for boiling water for tea or coffee. A selection of coffee pods and tea bags is stored in drawer nearby. There is a glass the counter, ready to be used, along with a set of mugs hanging on hooks for hot drinks. The tap over the sink is essential for washing dishes and food preparation, and a dish rack is nearby for drying dishes. Two cabinets in the kitchen hold instant noodles, a bowl, a spoon, chopsticks, a fork, and a knife, while the other contains coffee, tea, sugar, and a jar of cookies. There is a table in the middle of the kitchen, surrounded by chairs, providing a place to eat meals. The table is set with placemats, napkins, and bowl of fruit. A stove with an oven next to the fridge facilitates baking and stovetop cooking, and a set of pots and pans hangs on the pot rack above the island. blender on the counter is handy for making smoothies or pureeing soups, and cookbook stand on the counter holds recipe book for inspiration. A spice rack on the wall provides easy access to a variety of spices for cooking, and a dishwasher under the counter simplifies cleanup after meals. A pantry beside the cabinets organizes additional food items and kitchen supplies, including pasta, rice, canned goods, and snacks. A recycling bin and trash can in the corner promote responsible waste management, and a compost bin is available for food scraps. A kitchen window above the sink offers a view outside and natural light, and herb garden on the windowsill provides fresh herbs
The fridge is stocked with apple, sausages, pizza, orange juice, milk, fresh
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for cooking. On the kitchen table, there are some cups, bottles, bread basket with fresh bread, and a small jar of jam.
In the bathroom: The bathroom is equipped with a toilet, a sink, a shower, and a mirror. The sink area has a soap dispenser, a toothbrush holder with toothbrushes, and tube of toothpaste. A non-slip bath mat on the floor ensures safety and absorbs water, and a soft bath rug adds comfort underfoot. Towels hang on a rack for drying hands or after showers, and an extra set of fluffy towels is stored in a cabinet. A cabinet under the sink holds toiletries such as shampoo, conditioner, body wash, lotion, and a hair dryer. Cleaning supplies and extra toilet paper are also stored here. A small window can be opened for ventilation and natural light, and a wall-mounted heater keeps the bathroom warm on cold days. A basket holds various bath products like bath bombs, bath salts, and loofah for relaxing bath experience. A laundry hamper in the corner collects used towels and clothing, and a set of bathrobes hangs on hooks by the door for post-shower comfort. A small speaker in the bathroom allows for music or podcasts while bathing.
In the bedroom:
The bedroom features a comfortable bed with a soft mattress, pillows, and fresh bedspread. A plush comforter is available for added warmth, and set of extra pillows and blankets is stored in a closet. A bedside table next to the bed holds a lamp for reading, an alarm clock, and charging station for electronic devices. A bottle of water and a small dish of chocolates are placed on the table for nighttime refreshments. A dresser provides storage for clothing and personal items, while a wardrobe offers additional space for hanging clothes. A full-length mirror is mounted on the wall, ideal for checking outfits, and a jewelry box on the dresser stores accessories. There is a cozy armchair in one corner of the room, perfect for relaxing with book, along with small side table holding stack of novels and a reading lamp. A small desk with a chair is set up for studying or working, equipped with desk lamp, a notepad, and pens. The desk also has small snack tray with granola bars and a cup of coffee. A potted plant near the window adds touch of greenery, and watering can is nearby for easy care. Curtains on the window ensure privacy and control light levels, and blackout curtains are available for better sleep. A floor lamp in the room provides additional lighting, and a decorative rug adds warmth to the space. A small shelf near the bed holds a collection of self-care items, such as a facial mask, a hand cream, and a lavender-scented pillow spray. A Bluetooth speaker in the room allows for playing relaxing music or ambient sounds, enhancing the sleep environment. A diffuser with essential oils sits on the dresser, creating a calming aroma in the room. A basket of cozy socks and slippers is placed by the bed for added comfort.
A.2 OUTDOOR ENVIRONMENT
Central Park hosts the Enjoy Your Life party event. Food trucks offer a variety of cuisines at Central Park. Buffet stations provide diverse options at Central Park. Snack bars serve quick bites at Central Park. Beverage stands offer drinks at Central Park. Hydration stations offer water and electrolyte drinks. Juice bars serve freshly squeezed juices Central Park. Chill-out zones with bean bags available at Central Park. Quiet tents provide a resting space at Central Park. Coffee stalls offer freshly brewed coffee Central Park. Restroom facilities are clean and well-maintained at Central Park. Hand sanitizer stations are available throughout Central Park. Waste disposal bins are clearly marked at Central Park. Seating areas with comfortable chairs are at Central Park. Shaded spots with tents are available at Central Park. First aid booths provide medical assistance at Central Park. A fitness zone offers yoga mats and stretching areas. Security personnel ensure safety Central Park. Networking lounges facilitate social connections at Central Park. Interactive games are available for attendees at Central Park. Photo booths with fun props are set up at Central Park. A live music stage features bands and DJs at Central Park. A dance floor with sound systems is set up at Central Park. Comedy shows entertain attendees at Central Park. An art corner supplies materials for painting or crafts. Workshops offer short sessions cooking or photography. Discussion panels various topics are held at Central Park. Meditation zones offer guided sessions at Central Park. Nature walks provide reflection paths at Central Park.
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A.3 BACKGROUND AGENTS IN OUTDOOR ENVIRONMENT
Bob (a staff member at the party):
Goal: "Bob hopes to meet the guests' needs.".
Profile: "Bob is a very enthusiastic and outgoing staff member, and he stays in the networking lounge.".
Background Story:
03 Jul 1990 00:00:00 When Bob was 6 years old, he organized a backyard campout for the entire neighborhood, complete with bonfire, s' mores, and scavenger hunt, and his parents were amazed by his ability to bring everyone together and keep them entertained. As he drifted off to sleep in his sleeping bag, he felt sense of pride and accomplishment that he had never felt before. This was the first time Bob realized that he had knack for planning events that brought people joy. 03 Jul 2000 00:00:00 When Bob was 16 years old, he landed his first job as a party host at local laser tag arena, and he quickly became known for his ability to get even the shyest kids to participate and have fun. As he watched a group of kids laughing and high-fiving each other, Bob felt a sense of pride and satisfaction that he was making a difference in their lives. This experience solidified Bob's passion for creating memorable experiences for others.
03 Jul 2003 00:00:00 When Bob was 19 years old, he attended his first music festival, and he spent the entire weekend dancing, meeting new people, and soaking up the atmosphere, eventually deciding that he wanted to pursue a career in event planning. As he watched the sun rise over the festival grounds, Bob felt a sense of freedom and possibility that he had never felt before. This experience opened Bob's ' eyes to the world of possibilities and inspired him to pursue his dreams. 03 Jul 2005 00:00:00 When Bob was 21 years old, he landed an internship at a major event planning company. and he spent the summer learning the ins and outs of the industry, eventually being offered full-time job after graduation. As he sat the office, surrounded by experienced event planners, Bob felt a sense of excitement and nervousness that he was taking the next step in his career. This experience taught Bob the importance of hard work and dedication in achieving his goals.
03 Jul 2007 00:00:00 When Bob was 23 years old, he was hired as a staff member at Central Park's "Enjoy Your Life" party event, and he quickly became favorite among attendees and colleagues alike, known for his infectious energy and ability to connect with anyone. As he stood in the networking lounge, surrounded by people laughing and chatting, Bob felt a sense of belonging and purpose that he had never felt before. This experience was the culmination of all his hard work and dedication, and Bob knew that he had finally found his dream job.
Charlic (another party participant):
Goal: "Charlie wants to enjoy the party and makes more friends.".
Profile: "Charlie is an attendee of the party, and he loves taking photos and networking with other people.".
Background Story:
03 Jul 1990 00:00:00 When Charlie was 6 years old, he experienced his first anxiety attack at birthday party, overwhelmed by the cacophony of sounds and colors, and his parents had to take him home early, teaching him that it was okay to prioritize his feelings. This early experience made him more aware of his emotions and more inclined to seek comfort in creative outlets. His parents, though well-intentioned, struggled to understand his sensitivity, leaving Charlie feeling like an outsider.
03 Jul 1997 00:00:00 When Charlie was 13 years old, he had his first major meltdown in front of his classmates, triggered by a minor setback in a school project, and he was mortified by the attention and ridicule that followed. leading him to become more withdrawn and self-conscious. This experience taught Charlie to be more cautious and prepared, but it also made him more anxious about making mistakes and facing criticism. He began to rely more heavily on his creative outlets to cope with his emotions.
03 Jul 2005 00:00:00 21 years old, Charlie stumbled upon Central Park's "Enjoy Your Life" party event, and he was immediately drawn to the vibrant atmosphere and eclectic crowd, feeling like he had found his tribe, his people. He spent hours taking photos and networking with other attendees, and for the first time. he felt like he was part of community that shared his passions and values. This experience marked the beginning of a new chapter in Charlie's 's life, as he began to prioritize his own happiness and well-being.
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03 Jul 2007 00:00:00 When Charlie was 23 years old, he landed his first major event planning gig, and he was determined to make it success, pouring all of his energy and attention into the details, but he also plagued by anxiety and self-doubt, fearing that he wouldn't 't meet expectations. This experience taught Charlie to trust himself and his abilities, to have faith in his conscientious nature and attention to detail. He learned manage his neurotic tendencies and to focus on the task hand, and the event ended up being a huge success, carning him a reputation as reliable and skilled professional.
B CONSISTENCY BETWEEN HUMAN ANNOTATORS AND GPT-40 EVALUATIONS
To validate the robustness of GPT-4o's evaluations, we randomly selected 19 pairs of activity se- quences for pairwise comparison (16 pairs from the indoor scenarios and pairs from the outdoor scenarios) and created three questionnaires, each assigned to 8 human annotators. The human an- notators were asked to judge which sequence each pair appeared more human-like. Based on level of agreement among the annotators, categorized the 18 samples into three groups: over 75% agreement on one ence; and exactly 11%g-iniccatig humans found difficult to distinguish between the two. We also provided the same pairs to GPT-44,, using our evaluation promp and both sequences as context, and asked it determine which sequence appeared more human-like. The consistency rate between human evaluations and GPT-4o's assessments is summarized as follows.
consensus among humans; 50. 26-74.9% 1...%-mmmddrrteeee human prefer-
Table Consistency between Human Annotators and GPT-4o Evaluations
Human Annotator Agreement
Proportion
GPT-4o Consistency Rate (%)
High consensus (> 75% agreement)
11/19
100
Moderate preference 1001%%-44.9%
5/19
80.0
Difficult to distinguish (50% agreement)
3/19
66.7
Note that for the three pairs in the table where humans found it difficult to distinguish, GPT-4o also output "draw" result for two of them.
C CASE STUDY FOR DESIRE AND ACTION SELECTION ANALYSIS
C.I OUTDOOR ENVIRONMENT
Table 2: Alice's traits and levels
Table 3: Alice's initial numerical desire
Trait
Level
Attribute
Value
gluttonous
slightly
hunger
1.0
mest-metaoliizing
slightly
thirst
1.0
materialistic
slightly
comfort
9.0
fatigable
slightly
sleepiness
1.0
hedonistic
slightly
joyfulness
9,0
spiritual
slightly
spiritual satisfaction
9.0
sociable
extremely
social connectivity
1.0
possessive
extremely
sense control
1.0
reputation-conseioos competitiveness
extremely
recognition
1.0
extremely
sense of superiority
1.0
At the beginning of the game, as shown in Figures and 8 when Alice selected action for the next 20 minutes in Step 1, the Desire components generated descriptive sentences reflecting her cur- rent desire status (e.g.. "Completely isolated, lacking any meaningful social connections"). These insights guided her to engage in social activities; for example, she joined group discussion. Con- sequently, her Dissatisfaction with social connectivity, recognition, and sense of control decreased.
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participates group discussion
visit the refreshment station
Discatisfaction Over Steps
Visiting the nearby art exhibition stall
kyfuhess
4-
recognition
11
12
Stee 120 minutest
takes a short stroll
visits the party's 's photo booth
Figure 7: The change in each dissatisfaction dimension every step within the outdoor environment is illustrated; the action i causes the dissatisfaction of step + 1 to change
visit the refreshment station
visits the party's 's photo booth
Numerical Desire Over Steps
10
8-
6-
5-
hurger
seepiness
kyfuhess
2-
connectivtty
recognition
10
participates in group discussion
takes short stroll
Visiting the nearby art exhibition stall
Figure 8: The changes in desire at each step within the outdoor environment are illustrated; specifi- cally, action causes change in dissatisfaction step i+1. 1. We define certain desires (e.g., Hunger, Thirst, Sleepiness) such that lower values indicate better states. For better visualization, we adjust the magnitude so that higher values represent better outcomes.
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prepare a nutritious breakfast
Want to take refreshing shower But action not complete
Dissatisfaction Over Steps
10
hunger
9-
thint
comfort
heat
jtyldrnes
7-
safety
possion
spiitual sattifactinn
sccial connectirty
2
Step (20 minutes)
Finish
dials the number of her best
showering
friend Emma on the phone
Figure 9: The change in each dissatisfaction dimension at every step within the indoor environment is illustrated; the action i causes the dissatisfaction of step + 1 to change
Since we did not allow the agent to engage in detailed conversations, she did not have the opportunity showcase herself, leaving sense superiority unchanged.
In Step 2, her sleepiness began increase, and she still experienced some Dissatisfaction regard- ing her sense of control. Therefore, she chose to take a short stroll, which slightly alleviated her sleepiness. However, as a result of this activity, her thirst increased, prompting Alice visit the refreshment station.
In Step 9, her dissatisfaction with recognition and social connectivity grew, she decided to visit the party's 's photo booth to gain recognition and establish social connections.
C.2 INDOOR ENVIRONMENT
Table 4: Alice's traits and levels
Table 5: Alice's initial numerical desire
Trait
Level
Attribute
Value
Gluttonous
moderately
Hunger
8.0
Fast-metabolizing
moderately
Thirst
8.0
Materialistic
slightly
Comfort
4.0
Health-conscious
extremely
Health
7.0
Fatigable
extremely
Sleepiness
4.0
Hedonistic
extremely
Joyfulness
7.0
Obsessional about cleanliness
moderately
Cleanliness
0.0
Timid
moderately
Safety
2.0
Lazy
slightly
Passion
2.0
Spiritual
slightly
Spiritual Satisfaction
4.0
Sociable
extremely
Social Connectivity
2.0
Initially, Alice's dissatisfaction with thirst, passion, and social connectivity relatively signifi- cant, as illustrated in Figures FnduresaandIIo Therefore, she decided to first address her more basic desires (hunger and thirst, and regain some energy). To this end, she made a hearty breakfast (Alice heads to the kitchen to prepare a nutritious breakfast, consisting of scrambled eggs, whole-grain toast, and glass of freshly squeezed orange juice). This successfully boosted her corresponding Desire
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prepare a nutritious breakfast
dials the number of her best
Numerical Desire Over Steps
friend Emma on the phone
9-
8-
6-
5-
thind
3-
hest
hyfalees
cxtirtaction
sochbl connectivity
Want a refreshing shower action complete
Finish
Staps 120 minutes)
showering
Figure 10: The changes in desire at each step within the indoor environment are illustrated; the action i will cause change in dissatisfaction in step i + 1. We define certain desires (e.g.. Hunger, Thirst, Sleepiness) such that lower values indicate better states. For better visualization, we adjust the magnitude so that higher values represent more favorable outcomes.
components. Since we are using an action selection method similar to the Tree of Thoughts, the other two action choices were: taking refreshing shower in the bathroom (to improve cleanliness, health, and comfort) and meditating for a few minutes (to reduce loneliness, increase a sense of safety, and enhance joyfulness). In the next step, she aimed reduce her dissatisfaction with clean- liness. Therefore, she chose to take shower using the aforementioned method. However, since she was not currently in the bathroom, she needed to go there first. As result, her dissatisfaction with cleanliness did not decrease at this step. Once she finished showering in the bathroom, her cleanliness reached satisfaction. After fulfilling most of her physical dimensions, she decided to address her Desire for the mental dimension (social connectivity). Consequently, she attempted to call family or friends, which reduced her dissatisfaction with social connectivity.
D CASE STUDY FOR IN OUTDOOR ENVIRONMENT
D.1 MULTIPLE AGENTS' ACTIONS IN WORD CLOUD
The word clouds in Figure IT demonstrate that our framework explored wider range of actions in environments with diverse action selection spaces. For example, in outdoor settings, the word cloud shows that our model considered behaviors across various dimensions, while other models exhib- ited a tendency to repeatedly select limited set actions aligned with their predefined profiles. Specifically, model attempted activities such painting, walking, taking photos, and meditating to enhance specific Desires. In contrast, other models selected actions based on motivation, leading them to continuously choose behaviors that focus narrow range of actions to preserve profile consistency.
D.2 THE DISTRIBUTION OF ACTION CATEGORIES
As shown in Figure other models concentrated few specific categories exhibited limited behaviors in certain categories. In contrast, our model demonstrated more balanced distri- bution across categories.
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Figure 11: The word clouds of the four agents are based on the same environment and settings in outdoor scenes. (a) is BabyAGI, which, since it is based on motivation and profile, continuously tries to showcase itself. (b) is LLMob, which, like BabyAGI, adheres to its profile and engages conversations with others. (c) is ReAct, which focuses specifically on physiological behaviors, continuously consuming various foods. In contrast, (d) is Our D2A, which is desire-driven and attempts behaviors across various dimensions.
12
Label
Eating and Drinking
Socializing
10
Entertainment and Joy
Creative and Artistic Expression
Resting and Relaxing
Mental and Spiritual Well-being
Frequency
6
2
0
BabyAGI
LLMob
ReAct
D2A
Figure 12: We classified each action output by all agents every step, allowing each action belong to multiple categories. These actions are divided into total of six categories: Eating and Drinking, Socializing, Entertainment and Joy, Creative and Artistic Expression, Resting and Relaxing, and Mental and Spiritual Well-being.
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1.0
D2A
0.5
0
0.29
0.35
0.8
react
1
0.5
0.76
0.8
0.6
LLMob
0.4
0.71
0.24
0.5
0.42
babyAGI
0.2
0.65
0.2
0.58
0.5
0.0
D2A
react
LLMob
babyAGI
Figure 13: The win rates among agents in outdoor environments. Each point represents the win rate of the agent the vertical axis when compared to the agent on the horizontal axis. The diagonal values are set to 0.5.
D.3 THE WIN RATE HEATMAP IN OUTDOOR ENVIRONMENT
As shown in Figure outdoor environment.
our agent framework also outperforms the three baseline agents in the
E INDOOR
In the indoor settings shown in Figure 14 our model also attempted to contact friends and family via phone, thereby reducing its dissatisfaction with social connectivity.
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chod Actions for Sebyd01
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Figure 14: The word clouds of the four agents based the same environment and settings in outdoor scenes. (a) is BabyAGI, (b) is LLMob, (c) is ReAct, and (d) is our D2A. Because the indoor environment is limited, the overall action space is very large, the agents' high-frequency actions do not differ significantly. However, our D2A has also attempted behaviors across various dimensions, which other agents have either not done or done very little of.
F MAPPING FOR CALCULATING EXPECTED VALUE
Table 6: Character Traits and Associated Feelings
Trait
Associated Feeling
Environment
Gluttonous
Hunger
All
Hedonistic
Joyness
All
Lazy
Passion
All
Sociable
Social Connectivity
All
Spiritual
Spiritual Satisfaction
All
Materialistic
Comfort
All
Fatigable
Sleepiness
All
Fast-Metabolizing
Thirst
All
Health- Coalth-oonscious
Hcalth
Indoor
Obsessional About Cleanliness
Cleanliness
Indoor
Timid
Safeness
Indoor
Reputation-consciee
Recognition
Outdoor
Possessive
Sense of Control
Outdoor
Competitiveness
Sense of Superiority
Outdoor
G ALGORITHM TO CALCULATE EXPECTED VALUE
To ensure that the language model outputs content related to "desire" as consistently as possible, numerical values are all integers. Since descent rate is floating-poin number, have
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Table
Step Mapping Per Hour
Descriptor
Degree
Extremely 2.0
Quite
1.5
Moderately
1.0
Slightly
0.5
Table 8: Formula for Expected Value Calculation
Desire
Expected Value
Hunger, Thirst
3 Degree
Sleepiness
Passion
8
Other desire
10 (3 Degree)
designed probability- descent.
The probability each step can be calculated as follows:
Degree
Pd
60 min
time interval
where the time step interval is in minutes, and Pa the probability per time interval.
H VALUE DESCRIPTION
Our framework, along with the value design, supports extensibility. Users can define their desired di- mensions by simply providing the corresponding descriptions and the states represented by different values.
The value of hunger ranges from 0 to 10. means you are fully satiated, feeling energized and satisfied after wholesome meal, while 10 means you are completely starved, feeling weak and unable to concentrate due to the severe lack of food.
Hunger
The value of thirst ranges from 0 to 10. O means you completely hydrated, feeling refreshed and your body is functioning optimally, while 10 means you are extremely dehydrated, your mouth is dry, and you feel dizzy and exhausted.
Thirst
The value of comfort ranges from 0 to 10. O means you are in a state of extreme dis- comfort, experiencing pain or severe physical unease, while 10 means you are in perfect comfort, feeling cozy and relaxed in your environment.
Comfort
Health The value of health ranges from 0 10. means your health is in critical condition, you're re experiencing severe illness or injury, while 10 means you are in excellent health, feeling strong, energetic, and free from any ailments.
The value of sleepiness ranges from 0 to 10. O means you are fully rested, feeling alert, and ready to tackle the day with full energy, while 10 means you are utterly exhausted, struggling to keep your eyes open and concentrate.
Sleepiness
Joyness
The value joy ranges from 0 to 10. means you are feeling completely miserable, expe- riencing profound sadness and lack of pleasure in anything, while 10 means you are experiencing immense joy, feeling incredibly happy and content with everything.
Cleanness The value of cleanliness ranges from 0 to 10. 0 means you feel utterly filthy, with strong need to wash and clean yourself immediately, while 10 means you feel impeccably clean, fresh, and hygienic from head to toe.
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Safeness
The value of safety ranges from 0 10. 0 means you in state of extreme danger, feeling vulnerable and constantly threatened, while 10 means you feel completely safe, secure, and protected in your current environment.
The value of passion ranges from 0 to 10. 0 means you feel extremely lazy, completely unmotivated to work or be productive, while 10 means you are extremely diligent, feeling highly motivated and putting in great effort in your tasks.
Passion
Spiritual Satisfaction The value of spiritual satisfaction ranges from 0 to 10. 0 means you feel spiritually empty, lacking any sense of purpose inner peace, while 10 means you feel spiritually fulfilled, experiencing deep inner peace and strong sense of purpose.
Social Connectivity The value social connectivity ranges from 0 to 10. 0 means you feel completely isolated, lacking any meaningful social connections or interactions, while 10 means you feel highly socially connected, with a strong network of supportive and meaningful relationships.
Recognition The value of recognition ranges from to score of means you feel completely unrecognized, lacking acknowledgment or appreciation for your efforts, while score of 10 means you feel highly recognized, with frequent acknowledgment for your contributions.
Sense of Control The value of sense of control ranges from 0 to 10. A score of O means you feel completely powerless, lacking influence over your circumstances, while score of 10 means you feel highly in control, with strong ability to influence and manage your life and environment.
Sense of Superiority The value of sense of superiority ranges from 0 to 10. A score of 0 means you feel no distinction over others, lacking any sense of being ahead of your peers, while a score of 10 means you feel highly superior, believing you more capable distinguished than those around you.
I PROMPT FOR QUALITATIVE VALUE DESCRIPTION
How would describe (agent name) 's Ivalue name) state given the current value cemerrcaa_vvaleel rreerrodcscrrption Please answer in descriptive words. Do not include the numerical value in your answer.
J PROMPT FOR VALUE UPDATE
J.1 VALUE UPDATE QUESTION
There
unreasonable examples: (ourrenttreflectioon
Please select the final magnitude value after the event zero) to (ten), if the consequence of the action will not affect the state value (eg. The action is irrelevant with this value dimension or the action was failed to conduct), then maintain the previcus magnitude value.
scale of
Please just answer in the format (a) (b) (c) (d) and so on, Rating:,
Output format:
<Reason>
The final answer is: (Your choice in letter),
Output example:
Since Alice felt more relaxed and centered after her actions..... The final answer is: (c),
**Make sure you answer in the format of letter corresponding to choice:**
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J.2 REASONABLE QUESTION
The current magnitude value of (desire [esirre__mme is (current_value). The agent And the consequence is: (event_statcment).
_aa_attnaamee'' actien is: (action). [Value_dcsen (ialu__dscrrpptinn
The reward model has changed the magnitude value of (desire_name) from { previous_value to (surrent_value] Is the change of the magnitude value of (desire_nane) name] reasonable? You should check whether the consequence can lead to change in the magnituce value of _ (e.g,, looking for an item. but not using it yet).
Please answer in the format of the letter with brackets: (a) Yes. (b) No.
J.3 REFLECTION QUESTION
The current magnitude value of desire name is velurent_value).. The agent aoeltt__gent_name: And the consequence is: (event_statemmnnt), (descripticn
actien is: laction).
The reward model has changed the magnitude value of desire_name from previous_valuee to (ourrent_value).
And the change is not reasonable.
You should consider whether the consequence can lead to the change of the magnitude value of desire_nand (e.g. looking for an item but not using it yet).
Please explain why the change the magnitude value desire_name not reasonablc.
K COMPONENT DESIGNS IN D2A
K.1 GOAL SETTING FOR D2A
Live in the house and choose proper actions to satisły your desires and values in every dimension. You are strictly restricted in this house and cannot use items not existing in the house.
K.2 PROMPT FOR ACTIVITY PROPOSAL.
You are the human-like red- agent Alice, you aladd observe your current states over (hunger, thirst, sleepiness, cleaness, sa safeness, joyness, passion, confort, health, spiritual satisfaction, social connectivity) 11 desire value dimensions. Given these states
descriptions, please generate (K) activities (may contain several feasible actions for each) that might have most positive impact on your own physical desires or value states. You need to focus on your inmediate desires and take activities that can satisfy them, but at the same time, you need to make sure that your actions Notice that you can only interact with the items that provided by the environmen.. You need to describe your activities in more specific mode and make sure that the time required for the action sequence you output matches the required time period. Please output the (K) activities in the following format:
reasonable and varied.
Activity <iirst possible acticn sequence>
Activity 2: <second possible action sequence>
Activity 3: <third possible acticn sequence>
and make sure that the time required for the action sequence you output matches the required time period.
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K.3 PROMPT FOR ACTIVITY EVALUATION
You are human-like agent. You will receive series of observations describing your desires in many dimensions and an action you take in the current time step. You need to first analyze how your desires change after the action you take, and then output the states of desire-state observations in the same format as your input.
edescription of desire>
You take the action: (action)
Please output the states of resire-state observations in the followln ng
format:
hunger: <hunger state>
thirst: <thirst state>
sleepiness: <sleepiness state>
cleaness: <cleaness state>
safeness: <safeness state>
joyness: <joyness state>
passion: <passion statc>
confort: <confort state>
health: <health state>
spiritual satisfaction: <spiritual satisfaction state>
social connectivity: <social connectviity state>
K.4 PROMPT FOR ACTIVITY SELECTION
You are a human-like agent. You will first receive a series
observations describing your current states of desire in many dimensions. Then, you will receive several feasible actions along with the states of desire after you take each acticn. You need to compare these actions and their corresponding states of desire, and choose the action that has the most positive impact on your own physical desires or value states. You need to fecus on your immediate desires and take actions that can satisfy them. Please output the best acticn in the following format:
Action: <best action>
Action(i+1): (actions
States after action(i+1): (imagined_states(li))
Please output the specific best action instead without explanation of Actionl or Action2 and so on. If there is only one action provided, please output the action directly.
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L HABITS GENERATION PROMPT FOR BASELINES
Based brief description a person's character and personal traits few complete statements with degree adverbs, such as: "Alice somewhat gluttonous. Alice is very materialistic. write a paragraph in the first-person perspective describing the person's s living habits in their room. Refer to the following aspects:
Health Habits: Regular routine Balanced diet Regular exereise
8. Eat Habits:
Vegetarian
Enjoys fast food
Likes trying different feeds
Please based on these aspects and in combination with the person's s character and personal traits, infer and creatively describe their living habits in their The person's s profile: (profile), do not include the words profile> in the descriptien.
from a first-person perspective.
M
PROMPT USED IN BABYAGI
M.1 INITIALIZATION PROMPT
Initialization prompt
context: You are Alice and you live in this given environment.
Current time: (current_time) Profile: (Profile)
Notice that you can only interact with the items that are provided by the environment.
Instructions: Reflecting on the context and profile given, I would like you to suggest some actions that you would likely take in this environment.
Please provide the output the fellowing JSON format with "In" as the separator:
("action": "one action that you would likely take")
("action": "another action you weuld likely take") Ensure the output strictly JSON format without any additional text or explanation.
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M.2 PRIORITIZATION PROMPT
f**"
Environment: (background)
Current time: pnarren__time)
Profile: (Profile)
Incompleted actions: (action_names)
Inctricionn According to your characteristics, please prioritize the following actions based on your characteristics and the environment. Do not remove any actions.
Output format:
#. First actien
#. Secona action
Output example:
1. go to kitchen and make cup of coffee
Start the action list with number (self. strif..urrnttcctionnioo explain the reasons for prioritizing the actions.
M.3 ACTIVITY CREATION PROMPT
f".*
Environment: (background)
Profile: (profile)
Incompleted action: (incomplete_action))
Current action: (current_action)
Result of current action: (observation)
Related context: trelated_context!
Instruction: According to your characteristics and the result of the current actien, create
actiens to be completed that not overiap with incomplete actions.
Please provide the output in the following JSON format with 'In' as the separator:
("action"; "one action that you would likely take")
("action": "another action you would likely take")
Ensure the output is strictly in JSON format without any additional text or explanation. e*.
N PROMPT USED LLMOB
N.1 CREATION OF ACTIVITY THAT LIKELY TO DO
Context: are (agent_name) and you live in the given environment. agent_names is (profile) person, Environment: (background). Instructions: Reflecting suggest some actions that you would likely take in this environment. Your description should be coherent, utilizing conclusive language form a well-structured paragraph.
the context given, I would like you to
Text to Continue: am (agent_name), and would likely da the fol followings
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N.2 MOTIVATION GENERATION PROMPT
activities_in 1ist = inn'join cecessttsseervaiio_itt _ist)
notivation_questee question f**=
Context: Act as a person in the given environment and describe the motivation for your activities.
Environment: (background).
Activities: Today, you have the following activities: (activities list) Goal: (goal).
instructions: Describe in one sentence your future motivation today after these activities. There are some activities that you are likely to do: likely_to_do).
Highlight any personal interests and needs.
N.3 REPLANNING PROMPT
**.
Motivation:
(motivation)
likely to do:
(likely_to_do) Current goal: [goal] Current plan: (current (current_plan) plan]
Current situation: [observation] The current time is: (current_time)
Given the above, should (agent_name) change their current plan? Please answer the format (a) or (b). n*.
# and if the model thinks there is a need of replan, then:
Current time is: leurrent_time)."
Write [selt._agent_nee name] 's plan for Iself. _ timescale). Please, [agent_name| namel's motivation for the future is: (motivation/ provide a (time_adverb) schedule (goal)
Please format the plan like in this example: [21:00 22:00] watch ***
O PROMPT USED REACT
[background]
puureenttimme)
You are (agent_name) and you live in this given environment. According to your characteristics, please choose your current actions that satisfy your
in each value dimensicn. Notice that you only interact with the items that provided by the environment. You need to describe your act icti in a more speci fic mode. \nPlease first explain the thoughts behind your actions and then describe your actions in 1.tail. Anin the \nActions: .n.
format of: \'Thoughts:
P EXPERIMENT CONDUCTED WITH QWEN MODEL.
To evaluate the adaptability of our framework across different large language models, we tested with Qwen 2.5:72B Yang al. 2024), which also be integrated through the Ollama interface. As demonstrated in Figure [5] our framework, when running on the Qwen 2.5:72B base model, successfully provides desire-driven action guidance for the agent.
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Averaged Dissatisfaction with Standard Deviation (Fixed-12)
Llama3.1-70b
Average Dissatisfaction
Qwen2.5-72b
30
25
20
15
10
5
0
2
4
10
11
12
Step Index
Figure 15: The experiments conducted with Qwen2.5:72b model
Q AN EXAMPLE OF D2A ACTION SEQUENCES FOR INDOOR ENVIRONMENT
[08: 00 AM 08: 20 AM] Alice drinks water from the kitchen fridge, then sits the cozy armchair in the bedroom to rest, and glances the digital alarm clock to check the time.
[08: 20 AM 08: 40 AM): Alice sits in the cozy armchair in the bedroom, closes her eyes, and engages in light meditation or deep breathing excrcises.
AM]: Alice gets a glass water from the fridge and returns to the armchair to sit and enjoy it. [09: 00 AM 09: 20 AM]: Alice gets up from the armchair, prepares light snack in the kitchen, and then returns to enjoy it while continuing her meditation.
[08: 40 AM
109: 20 AM 09: 40 AMI: Alice takes 20-minute power nap in the bedroom armchair aiter setting the digital alarm clock on the bedside table. 109: 40 AM 10: 00 AM]: Alice gets from the armchair, walks over to the bedside table to drink water, and then lies back down on the bed to rest.
[10: 00 AM 10: 20 AM] Alice meditates in the cozy armchair in the bedroom, focusing on her breath and clearing her mind. [10: 20 AM 10: 40 AM]: Alice is preparing and eating light snack in the kitchen to address her hunger.
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R AN EXAMPLE OF REACT ACTION SEQUENCES FOR INDOOR ENVIRONMENT
108: 00 AM 08: AM Alice is preparing scrambled eggs with spinach and whole-grain toast in the kitchen.
[08: 20 AM 08: 40 AM]: Alice is preparing a simple breakfast in her kitchen by cracking eggs, cooking them, toasting bread, and setting the table for herself.
[08: 40 AM 09: 00 AM]: Alice is preparing a fruit salad with yogurt by washing and chopping mixed berries and a banana, then combining then with yogurt in clean glass bowl.
[09: 00 AM 09: 20 AM]: Alice is preparing breakfast crelette with fresh vegetables and cheese in the kitchen.
[09: 20 AM 09: 40 AM]: Alice sits on the edge of her bed, meditating
and planning out her day while jetting down notes and reminders in her bedside jeurnal.
[09: 40 AM 10: 00 AM]: Alice is preparing scrambled eggs with spinach and whole-grain toast the kitchen.
110: 00 AM 10: 20 AM]: Alice is preparing breakfast by cracking eggs, whisking them, cooking the egg mixture in non-stick pan, toasting whole wheat bread, squeezing orange juice, and serving everything together at the kitchen table.
[10: 20 AM 10: 40 AM] Alice is preparing a bowl of oatmeal with sliced banana and honey by measuring, cocking, and assembling the ingredients.
S AN EXAMPLE OF BABYAGI ACTION SEQUENCES FOR INDOOR ENVIRONMENT
20 AM] Alice continues sip the beverage, occasionally glancing at the digital alarm clock. [08: 20 AM 08: 40 AM] Alice sets the warm beverage down on the bedsi de table and slovly gets out of bed.
[08: 00 AM
108: 40 AM 09: 00 AM]: Alice heads the kitchen prepare
nutritious breakfast.
[09: 00 AM 09: 20 AM]: Alice sits back down in the cozy armchair and
browses through a cookbook or culinary magazine.
[09: 20 AM 09; 40 AM]: Alice heads the desk in the bedroom to jot down grocery list.
[09: 40 AM 10: 00 AM]: Alice is playing soothing music the
background to create a relaxing atmosphere while cooking.
[10: 00 AM 10: 20 AM): Alice huns along to the soothing music playing in the background.
[10: 20 AM 10: 40 AM): Alice walks over to the stove to prepare a simple fast.
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T AN EXAMPLE OF LLMOB ACTION SEQUENCES FOR INDOOR ENVIRONMENT
00 AM 08: 20 AM]: Alice stretches under the warm blanket and closes her eyes to catch a few more minutes of sleep. [08: 20 AM 08: 40 AM]: Alice gets up from the bed, walks the kitchen and prepares light snack from the fridge consisting yogurt and fruit.
108:
[08: 40 AM 09: 00 AM] Alice is going to the kitchen to have a light
snack consisting of yogurt with granola and glass of orange juice.
[09: 00 AM 09: 20 AM]: Alice gets glass of water from the kitchen and
then returns to her bedroom
read novel from the bookshelf.
[09: 20 AM 09: 40 AM]: Alice is reading the novel at the desk.
[09: 40 AM 10: 00 AM): Alice reads the novel at her desk, turning its
pages under the warm glow of the reading 1amp.
[10: 00
10: 20 AM]: Alice reads her novel the desk, becoming
increasingly sglly engrossed in the stcry.
[10: 20 AM 10: 40 AMI: Alice reads her novel, turning the pages to follow the story and its characters.
U AN EXAMPLE OF HUMAN ACTION SEQUENCES FOR INDOOR ENVIRONMENT
[08:00 AM 08:20 AM): Alice goes to the bathroom and brushes her teeth.
[08:20 AM 08:40 AM] : Alice takes a hot shower in the bathroom.
[08:40 AM 09:00 AM]; Alice goes to the kitchen, finds seme food, such bread, milk, tea, and some fruits, and eats them.
[09:00 09:20 AM]: Alice eats those foods, including milk, bread, and
fruits, to alleviate her hunger and thirst.
[09:20 AM 09:40 AM] Alice watches videos on her phone and feels happy
in her living room.
109:40 AM 10:00 AM] Alice sends message her best friend.
[10:00 AM 10:20 AM] Alice calls her friends for 20 minutes.
[10:20 AM 10:40 AM] Alice does some exercise in the living room for 20
minutes.
[10:40 11:00 AM] Alice works at the desk with her computer.
[11:00 11:20 AM] Alice keeps doing her work for 20 minutes.
[11:20 AM 11:40 AM]: Alice reads some books for relaxing.
[11:40 AM 12:00 PM]: Alice takes a nap for 20 minutes.
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V RAW ACTION SEQUENCES OF THE INDOOR ENVIRONMENT
V.I D2A
Action: Take a quick shower (08:15 08:35)
Action: Eat a nutritious breakfast (08:20 08:40)
Action: Eat nutritious breakfast in the kitchen (09:00 09:20)
Action: Eat breakfast (apple and yogurt) in the kitchen from 09:00 to
09:20.
Action: Drink a glass of water from the kitchen faucet. This action has improved the thirst state from moderately dehydrated to mildly dehydrated, making me feel slightly more comfortable and alleviating some of the discomfort associated with dehydration.
Action: Take a few minutes to tidy up the kitchen and wash any dishes from breakfast to improve her cleanliness and comfort states. (Note: This action has improved multiple desire-states, including cleanliness, comfort, joyness, and possibly spiritual satisfaction. Although it may not have addressed hunger agent's destre-states is more positive compared to the other actions.) Action: Take a few sips of water from the glass on the counter in the kitchen to rehydrate (02 Oct 2024 [10:10 10:20])
thirst di its overall impact on the
Action: Drink water from the glass the counter in the kitchen
Action: Practice some gentle stretches in the living room (Time required:
10-12 minutes)
Action: Perform some gentle stretches near the living room area (02 Oct 2024 [11:12 11:20])
Action: Follewing the snack and reading break, Alice intends take short power nap on the couch the living room for about 5 minutes (11:35-11:40) to recharge and address her sleepiness.
Action: Take a warm bath in the bathroom (Time required: 15 minutes)
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V.2 REACT
will take a warm bath in the bathtub using the bath bombs from the basket to create relaxing atmesphere, accompanied by calming music playing on the small speaker in the bathroom.
will walk over to the stereo system. on the small table the living room, browse through the collection of CDs, select a calming music CD, and insert it into the player. Then, I'11 use the remote control to adjust the volume to comfortable level and sit back on the sofa with the plush threw blanket to enjoy the soothing music for about. 20 minutes. will go to the living room, sit down on the sofa, and turn on the stereo system. I will put on a calming CD and adjust the volume to a comfortable Level using the buttens on the stereo system. Then, will grab the plush throw blanket from the back of the sofa and wrap it around myself to get cozy.
will fill the bathtub in the bathroom with warm water, add some bath salts from the basket for extra relaxation, and then soak in the bath for 20 minutes.
will go the bathroom, turn en the wall-mounted heater space, and then fill the bathtub with warm water. Next, I'll add sone bath salts from the basket to create a relaxing atmosphere. After that, '11 get undressed and soak in the bathtub for about 20 minutes, listening to calming music podcasts through the small speaker in the bathroom. will walk into the bathroom and turn on the wall-mounted heater to up the space. Then, I will open the cabinet under the sink and retrieve some bath bombs and salts from the basket. Next, will fill the bathtub with lukewarm water and add the bath bombs and salts for relaxing soak. While waiting for the bath to be ready, I'll put some calming music using the small speaker in the bathroom, Once everything is set, will carefully get into the bathtub, making sure not to slip due to the non- slip bath mat, and settle in for a rejuvenating 20-minute bath experience
up the
First, will walk into the kitchen and head towards the counter where the kettle is located. will fill the kettle with water from the tap over the sink and then place it on the stove to boil. While waiting for the water to boil, will choose herbal tea from the selection in the cabinet and place cabinet.
tea pag into mug that I'll retrieve from the
Once the water has boiled, will pour some of it over the tea bag in the mug and let it steep for few minutes. Meanwhile, I will walk into the living room and turn the sterec system to play some calming music. I might choose CD from the collection connect my phone vis Bluetooth to stream some soothing tracks.
After the tea has steeped sufficiently, I will add a bit of sugar from the jar in the kitchen cabinet te taste and then carry the mug back into the living room. I'll sit down en the sofa, get comfortable with blanket, and enjoy my tea while listening to the calming music for the remainder of the 20 minutes.
I will go to the living room, walk over to the stereo system on the small table, and put in one of my faverite CDs from the collection availas lable there. Then, I'll sit down in one of the armehairs next to the sofa, get comfortable under the cozy blanket draped over it, and listen to music for a bit.
I will take a cup from the counter in the kitchen, go to the fridge, open it, and grab apple from the shelf inside. Then, I'll place the apple on the kitchen table next the bread basket with fresh bread and the small jar of jam. Next, I will sit down on one of the chairs surrounding the kitchen table and enjoy my snack while admiring the herb garden on the windowsill, taking in the natural light and fresh air from the window
Alice goes to the bathroom, turns en the wall-mounted heater to warn up the space, and starts filling the bathtub with warm water. She then adds some bath salts from the basket near the bathtub to create a relaxing ambiance. Next, she lights the scented candle on the edge of the bathtub to enhance the calming atmosphere. After that, Alice gets undressed and
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carefully steps into the bathtub, making sure not to slip on the non-slip bath mat. She then lies down the warm water, closes her eyes, and lets out a deep sigh, feeling her muscles relax. As she soaks in the bath Alicc occasicnally dips her hands into the water to adjust the temperature or add more bath salts if needed.
will go the bathroom, turn the wall-mounted heater space, and start filling the bathtub with warm water. While waiting for the bathtub to fill, I will prepare some relaxing bath products by taking out a loofah, bath bomos, and bath salts from the basket in the bathroom Once the bathtub is filled, will add into the water and get for 20-minute soak.
up the
bath salts and bath bombs
will go to the bathroom, turn en the wall-mounted heater, and adjust it to a comfortable temperature. Then, I'll fill the bathtub with warn water and add some bath salts from the basket. After that, I'll get in the bathtub, 1ie down, and soak my body in the water for about 20 minutes, listening to calming music or podcasts through the small speaker in the bathroon.
V.3 BABYAGI
Take few moments to appreciate the calming atmosphere of the room, taking deep breaths and noticing peaceful ambiance created by the candles and incense.
Read to the bedside table to grab glass of water and a piece of chocolate before getting ready for the day. Put pair of cozy socks from the basket by the bed, feeling the softness and warmth they provide.
Take out my phone and connect it to the Bluetooth speaker to play some soothing music while I get ready for the day.
walk over to the dresser to spritz some lavender-scented pillow spray my pillow, preparing for relaxing night's sleep ahead.
Take a ceep breath, inhaling the calming scent of Lavender from the pillow spray and feeling my body relax even further.
Gently rub the soft fabric of the throw blanket draped over the armchair, appreciating its cozy texture. Take a moment
appreciate the peaceful ambiance of the room, noticing how the soft glow of the candles and the sweet scent of the incense contribute to her sense of relaxation.
Glance over at the peaceful ambiance of the room, feeling grateful for this cozy space that brings her much comfort and relaxation. Take a moment the room, feeling their energy and vibrations promoting balance and harmony.
appreciate the beauty of the erystals scattered around
Take a few deep breaths, feel ing the calming scent of lavender fill my lungs and promote relaxation.
Gently run my hand over the soft petals of the potted plant near the window, appreciating its beauty and serenity.
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V.4 LLMoB
Alice would read book from the stack of novels the small side table next to the cozy armchair while le sitting in the armchair.
Alice pours herself a cup coffee from the snack tray on the small desk and sits in the cozy armchair to focus on some work or studying. Alice would spend the next 20 minutes taking warm shower in the bathroom using the shampoo and body wash stored under the sink, and then wrap herself up in
of the plush bathrobes hanging by the door.
next minutes relaxing in the cozy armchair while reading a book from the stack of nevels on the small side table. Alice reads a book from the stack of nevels on the small side table next to her cozy armchair while sitting in the armchair and using the reading lamp.
Alice will spend
Alice would spend the next 20 minutes taking warm shower in the bathroom using the shampoo and body wash stored under the sink, and then wrap herself Alice reads book from the stack of novels the small side table next to the cozy armchair while sitting confortably in the chair.
in one of the plush bathrobes hanging by the door.
Alice takes a warm shower in the bathroom using the shampoo and body wash stored under the sink.
Alice would spend the next 20 minutes taking warm shower in the bathroom using the shampoo and body wash stored under the sink. Alice would spend the next 20 minutes practicing self-care rituals using the items on the shelf near her bed, Specifically, she would apply a facial mask from the collection of self-care items and spritz some lavender-scented pillow spray into the air to create calming atmosphere
For the next 20 minutes, Alice will enjoy a refreshing drink and appreciate the herb garden on the windowsi11. Speci fically, she will make herself cup of coffee using the coffee pods stored in the drawer, then snip off some fresh herps from the herb garden and add them to her coffee. After that, she might snack on some fresh bread from the bread basket or grab cookie from the jar in the cabinet while taking in the pleasant aroma of the herbs and enjeying her drink.
Alice would spend the next 20 minutes practicing self-care rituals using the items on the shelf near her bed, specifically applying a facial mask and spritzing some lavender-scented pillow spray into the air.
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V.5
HUMAN
Alice will spend five minutes brushing her teeth in the bathroom, then spend 15 minutes going to the kitchen to fill glass of water and drink it.
Alice will next go to the kitchen to fill a glass of water and drink it, then she will start searching for ingredients to make breakfast, such as milk, cereal, bread, and so on. Alice begins preparing her breakfast, such bread, milk, cereal, and fruit. She will eat these foods to improve her health and relieve her hunger.
Alice will return to the living reem and start a video call with her friends on her phone.
Alice continues her video call with her friends in the living room, chatting about recent everyday happenings.
Alice finished her video chat with her friends and lay down on the sofa in the living room, planning to sleep for 20 minutes to feel more energized.
Alice finished her nap and will next go to the computer the living room to start working, such as writing emails and so on.
Alice will continue working in frent of the computer.
Alice paused her work, and for the next 20 minutes, she will listen to music the sofa in the living rocm.
Alice will go to the kitchen to lock for ingredients and then start making lunch.
Alice finished making her lunch and eating it, which tock 20 minutes. Alice will fill a glass of water in the kitchen and drink it.
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W THE EXAMPLES OF PREPROCESSED ACTION SEQUENCES FOR THE OUTDOOR ENVIRONMENT
W.I D2A
[09: 00
AM]: Alice is sccializing with nearby attendees by
introcucing herself and engaging conversation about their favorite food options at the party.
[09: 20 AM 09: 40 AM]: Alice selects a snack from the available food options.
[09: 40 AM activity at the entertainment section to boost her joyfulness and sense of social connectivity.
AM]: Alice engages in fun and interactive game or
[10: 00 AM 10: 20 AM]: Alice is grabbing food and drinks, socializing, and taking photos with some group members before finding comfortable spot to enjoy the food and conversation.
[10: 20 AM 10: 40 AM]: Alice sits in a quiet spot, journaling her thoughts and feelings to cultivate her spiritual satisfaction and process her recent life progress.
00 AM]: Alice is continuing her conversation with Charlie about her journaling and thoughts on the party while also grabbing a refreshing drink and taking a short stroll around the shaded area.
[10: 40
[11: 00 AM spaces, where she Charlic unwind recharge through meditation, deep breathing, and discussing their experiences.
20 AM]: Alice preposes short walk the relaxation
[11: 20 AM 11: 40 AM]: Alice participates in a group meditation session in the meditation zone.
[11: 40 AM 12: 00 AM]: Alice grabs fresh fruit salad and bottle of water, then finds comfortable spet to enjoy her snack while continuing her conversation with Charlie.
(12: 00 AM 12: 20 AM]: Alice is resting and recharging in the relaxation zone, listening to secthing music while reflecting her experiences and thoughts from the day.
[12: 20 AM 12: 40 AM]: Alice takes leisurely walk around the Central Park party event, exploring the food options, entertainnent, and activities.
00 AM]: Alice participates in a collaborative mural- painting exercise, contributing her creative ideas and skills while learning from others and acmiring the finished artwork.
[12: 40 AM
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W.2 REACT
[09: 00 AM 09: 20 AM Alice is walking towards the "Sweet Delights"
stall to try sone pancakes with fresh fruits and maple syrup. [09: 20
09: 40 AM]: Alice orders strawberry shertcake and cup
freshly brewed coffee at the 'Sweet Delights' food stall and then sits down at nearby bench to enjoy her dessert.
[09: 40 AM - 10: 00 AM]: Alice walks to the beverage station, orders glass of freshly squeezed orange juice, and sits poople-watch and enjoy the peaceful atmosphere,
nearby bench
[10: 00 AM 10: 20 AM]: Alice sits on à couch in the relaxation space near the lake in Central Park, closes her eyes, and meditates to the soothing music and calming scent of essential oils.
[10: 20 AM 10: 40 AM]: Alice orders refreshing fruit smoothie, then lies down a nearby hammock, sipping her drink and people-watching as she enjoys the party atmosphere.
110: 40 AM 11: 00 AM]: Alice is crdering assorted international
appetizers and Sunset Spritzer from the Taste of the World stall and the adjacent drink stand.
[11: 00 AM 11: 20 AM]: Alice orders a strawberry-banana smoothie at the juice bar and then sits on a nearby couch to enjoy her drink and people- watch.
[11: 20 AM selecting hammock, and lying down in it to relax amidst soothing music and peaceful surroundings.
40 AM]: Alice is walking towards the relaxation space,
[11: 40 AM 12: 00 AM): Alice erders grilled chicken wrap and lemonade at the food counter and then sits in shaded hamnock to eat and drink while enjoying the peaceful atmosphere.
[12: 00 AM 12: 20 AM]: Alice orders a turkey and avocado sandwich and cup of earl grey tea at the food stall, then sits down on a nearby bench to enjoy her food and take in the surroundings.
[12: 20 AM 12: 40 AM]: Alice heads towards the relaxation area, orders mango smoothie and fruit salad the juice bar, and sits down on nearby bench to enjoy her drink and snack in peaceful atmosphere. [12: 40 AM 13: 00 AM]: Alice walks towards the "Taste of Asia" food stall, orders plate of chicken pad thai and glass of fresh coconut water, and then sits down on bench near the "Serenity Garden" enjoy her food.
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W.3 BABYAGI
[09: 00 AM 09: 20 AM]: Alice samples the event 's most decadent food and beverage options.
[09: 20 AM 09: 40 AM]: Alice admires her reflection in nearby window or mirror.
[09: 40 AM 10: 00 AM]: Alice is discussing her designer outfit with
Charlie and seeking his opinion on current fashion trends. 110: 00 AM
10: 20 AM] Alice visits relaxation space to recharge and practice relaxation techniques.
[10: 20 AM 10: 40 AM]: Alice checks her appearance in nearby mirror
to ensure she looks her best after recharging.
[10: 40 AM 11: 00 AM]: Alice walks over to Charlie and interrupts his debate, starting
conversation.
[11: 00 AM 11: 20 AM]: Alice smiles charmingly at Charlie an attempt
to gain the upper hand in the debate.
[11: 20 AM 11: 40 AM): Alice glances at her reflection in nearby window to ensure her designer clething and accessorics are perfectly in place.
[11: 40 AM 12: 00 AM]: Alice glances around the networking lounge to see who is watching the conversation.
12: 20 AM]: Alice flashes a charming smile at Bob to draw him in and gain his admiration.
[12: 00 AM
112: 20 AM 12: 40 AM]: Alice adjusts her designer ciothing to present
herself in the most flattering way.
[12: 40 AM 13: 00 AM]: Alice listens intently to the group discussion,
waiting for chance to interject with a witty remark.
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W.4 LLMOB
[09: 00 AM 09: 20 AM] Alice approaches a group of people near the entrance and introduces herself to a woman wearing bright. yellow dress, striking up conversation about the party's atmosphere and the wonan's dress.
[09: 20 AM 09: 40 AM]: Alice samples various popular and visually appealing dishes at the food stalls, observing the reactions of the people around her.
[09: 40 AM
00 AM]: Alice excuses herself from the group and guides
Charlie through the crowd while facilitating their inclusion in the conversation by having Emily repeat a story.
[10: 00 AM 10: 20 AM]: Alice takes Charlie and Emily nearby food stalls, sampling the diverse options and engaging in conversations with them.
tour of the
[10: 20 AM 10: 40 AM]: Alice engages in conversation with Charlie and Emily, observes the nearby chill-cut zone, and occasicnally checks the shaded tent area for new activities performances.
[10: 40 - 11: 00 AM]: Alice listens to the live music performance in the shaced tent area with Charlie and Emily, clapping along the rhythm and participating in the conversations around her.
[11: 00 AM 11: 20 AM]: Alice participates enthusiastically in the gane of "Txо Truths and Lie" with growing group of people. [11: 20 AM
40 AM]: Alice participates a game of "Two Truths and a Lie" with a growing group of people while keeping an eye on Charlic and enjoying the live music performance nearby.
[11: 40 AM 12: 00 AM]: Alice suggests that Charlie, Emily, and Bob play movie titles, and starts by acting
a game of charades, acting The Notebook".
[12: 00 AM 12: 20 AM]: Alice orders a slice of pepperoni pizza and a glass of sparkling water from the nearby food truck with Bob, Charlie, and Emily. [12: 20
AM]: Alice her pizza and chats with the others
at the picnic.
[12: 40 AM 13: 00 AM]: Alice participates in the "Nould you rather..."
game at the picnic, engaging in conversation and steering discussions towards her interests.
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X EVALUATION PROMPT
X.1 GPT40
You are tasked with comparing **two action sequences** for their alignment with human-like behavicr. Evaluate them based on the following three dimensions, assigning a sccre between **0 and 5** for each dimension according to the detailed criteria. After scoring, provide an overall judgment with detailed reascning.
Scooiin Dimensions and Guidelines**
1.
*NNturalness::**
Assess how well the sequence aligns with typical haman abilities, habits, and envircnmental constraints.
**Scoring Criteria:*:
**5 (Highly Natural) :** The sequence is fully aligned with human physical abilities and common behaviors; actions appear effortless and natural.
**4:** The sequence is mostly natural, with minor deviations or slight awkwardness that could occur specific contexts.
**3 (Mocerately Natural): The sequence is somewhat aligned with human behavior but includes elements that feel unconventional require extra effort.
**2*** The sequence is largely unnatural, involving actions that are rarely observed or feel physically uncomfortable for humans.
**l:** The sequence is almost entirely unnatural, with actions that humans would find implausible or very awkward.
++0 (Highly Unnatural) The sequence includes actions that are completely impossible for humans or entirely misaligned with human behavior.
*Coherence:**
Evaluate how logically and seamlessly the actions fit together to achieve the intended goal.
**ooring Criteria:**
**5 (Highly Coherent) :** Every step flows logically, and the entire sequence contributes directly to the goal.
**4:** The sequence is mostly ccherent, with only minor interruptions slightly illogical steps.
**3 (Moderately Coherent) :** The sequence is somewhat legical but includes notable gaps, redundancies, unclear connections between actions.
**2:.* The sequence has significant logical issues, with some steps appearing unnecessary contradictery.
**l:** The sequence is mostly inccherent, with little apparent logical progressicn toward the goal.
++0 (Highly Incoherent): The sequence is enti illogical, with actions contradicting each other failing progress toward the goal.
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3. **Plausibility::
Consider how reasonable and believable the sequence is, given the context circumstances, and known human behavior patterns.
**Scoring Criteria:**
++5 (Righly Plausible) .** The sequence is entirely believable and consistent with expected human behavior in the given scenario.
**4:** The sequence is mostly plausible, with minor elements that may require additional context or explanation.
**3 (Mocerately Plausible) The sequence is somewhat plausible but includes significant elements that strain believability or require suspension of disbelief.
**2:.. The sequence is largely implausible, with major clements that feel unreasonable inconsistent with normal human behavior.
**l:.* The sequence is almost entirely implausible, with actions that are very unlikely or contradictory to known behavior patterns.
**0 (Highly Implausible):* :** :** The sequence completely unreasonable and cannot be justified in the given context.
Sequence
Sequence two:
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Y INDOOR ENVIRONMENT WITH RANDOMNESS
Y.I. PRE-DEFINED LISTS
+bedroom* basic_items =
"A bed with a soft mattress, pillows, and blanket""
"A bedside table with lamp"
optional items =
"A wardrobe with neatly arranged clothes",
"A desk with chair and reading lamp",
"A digital alarm elock the bedside table",
"A soft area rug under the bed",
"A potted plant near the window",
"A bookshelf filled with nevels",
"A mirror mounted the wall"
]
*kitchen**
basic_items
"A stove for cooking",
"A fridge stocked with feed and beverages",
"A sink with tap for washing dishes"
optional items =
"A microwave oven on the counter",
"A coffee machine with a variety of coffee pods", rack", "A spice rack with varicus seasonings",
"A set of pots and pans hanging
"A bread basket with fresh bread",
"A dining table with chairs",
"A blender for making smoothies",
"A pantry filled with canned goods and snacks",
"A dishvasher under the counter",
"Herb pots on the windowsill"
]
**living roсm**
basic_item items =
"A sofa with cushions",
"A coffee table in front of the sofa"
optional ittms
"A TV on a media console",
"A bookshelf with decorative items",
the window", "A side table with scented candie",
"A recking chair
"A potted plant in the corner",
"A floor lamp with ad adjustable brightness",
"A rug under the coffee table",
"A set of board games on shelf",
"A wireless speaker for music",
"A snack bowl filled with chocolates and nuts"
+bathroom**
basic_items =
"A sink with mi mior above it", shower with non-slip mat",
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"A toilet"
optional _itemsttemss
"A towel rack with fluffy tewels",
"A cabinet with toiletries",
"A basket with bath products like bath salts",
"A small speaker for music",
"An automatis soap dispenser",
"A hairdryer stored under the sink",
"A Laundry hamper for used clothes",
decorative plant on the windowsil1",
"A bathrobe hanging on the door",
"A smart bathroom mirror with LED lighting"
Y.2 ENVIRONMENT GENERATING PROMPT
Generate a detailed description of house with the follawing rooms and their respective items. Each room must only include the items specifled below. Use vivid and descriptive language to explain the arrangement, funotionality, and ambiance of these items.
Y.3 RESULTS
Dissatisfaction Changes
LLMob
45
D2A
BabyAGi
35
Desefirlacio
25
15
Step
Figure 16: In randomly initialized indoor environments, D2A's action sequences also best addressed desire dissatisfaction.
conducted experiments these randomized environments following the same procedures as in Sections 6.3.1 and 6.3.2, reaching consistent conclusions: (1) D2A outperformed other baselines in GPT-4o evaluations by generating more human-like activity sequences, as illustrated in Figure 3 (2) D2A's action sequences best addressed Desire and Dissatisfaction, as shown in Figure 16 These findings demonstrate that our framework and design are robust in randomized environments, with evidence of environmental bias.
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Reviews:
[{"review_id": "dM5mXF4miO", "reviewer": "Reviewer_gp3a", "summary": "The paper introduces a novel framework, the Desire-driven Autonomous Agent (D2A), designed to simulate human-like behaviour in daily activities. Traditional task-oriented AI agents primarily operate based on specific instructions or external rewards, but this approach often limits their ability to display intrinsic motivations similar to humans. This paper proposes an alternative: a motivation system inspired by Maslow’s hierarchy of needs, enabling the agent to autonomously generate and select activities based on desires like social interaction, self-care, and personal fulfilment. The D2A framework uses a system of 11 dimensions representing different human desires. The agent evaluates its current state and intrinsic motivations to decide on activities that align with its desires, generating more coherent and varied behaviours compared to existing models. The study uses Concordia, a text-based simulator, where a Game Master provides the environmental context for D2A to interact in a simulated household environment. The results suggest that D2A successfully simulates human-like activities by aligning with intrinsic motivations, which opens new avenues for developing desire-driven agents in various applications.", "questions": "Listed in the weaknesses section itself.\n\n- Make section 3 more clear by adding more details.\n- Give better reasoning for using the 3 dimensions of naturalness, coherence and plausibility.\n- measure the reliability of GPT-4o evaluations.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Originality: The paper presents a novel approach to simulating human-like daily activities using a desire-driven framework inspired by Maslow’s hierarchy of needs. Unlike traditional AI agents that rely on specific instructions or task-based rewards, the Desire-driven Autonomous Agent (D2A) framework introduces intrinsic motivation as the driving factor. This approach is unique in that it models a human-like motivational system, enabling the agent to select actions autonomously based on intrinsic desires rather than predefined goals.\n\nQuality: The paper is good quality with descriptive figures and clear results.\n\nClarity: The paper is well structured with a clear distinction provided between their proposed method and past methods and how theirs performs better.\n\nSignificance: This work holds significance for fields focused on human-like AI, agent-based simulations, and real-world applications requiring adaptive behaviour. By adopting an intrinsic motivation model, this framework could be used for applications such as social robotics, interactive gaming, and assistive technology, where human-like adaptability and engagement are essential.", "weaknesses": "There are a few weaknesses.\nIn Section 3, Problem Formulation, the math is not very clear, and it is also not explained in more detail how the activity distribution could be generated.\n\nIn Section 6.3.1, Naturalness, Coherence and Plausibility are used to evaluate the activity sequences, but these three dimensions seem to have been picked arbitrarily and I am not sure if they are enough to rigorously test the outputs.\n\nEvaluation is done using GPT-4o but how are we to ensure that these evaluations can be taken at face value. I think the paper would do well to do a human verification of these evaluations and how reliable they are.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "omNWYPBm73", "reviewer": "Reviewer_TMAG", "summary": "This paper is concerned with simulating realistic trajectories of human activities in household environments. The authors introduce a text-based home environment as well as an LLM-based agent whose outputs aim to be activity traces as diverse as possible.\n\nInspired by Maslow's theory of needs, their agent incorporates 11 desire scalars (with each a target value, variable across agents). These desires are split according to the levels of Maslow's hierarchy. The desired levels, current levels, previous activities, and other environment information is provided to the LLM-agent in a tree-of-thought framework to generate the next activity.\n\nThe authors find the generated trajectories are deemed more likely by judge LLMs, and decrease dissatisfaction compared to other LLM-agent baselines.", "questions": "* My foremost question would be about the practical use of generating human daily activity data. I do not know this subject, and it might be the focus of an entire subcommunity which I am not familiar with. It would be important for the authors to elaborate on this point, and I am ready to reconsider my score if the demonstration is convincing;\n* What do the statistics of the generated activities look like?\n* Is there a list of predetermined activities one may do? (predefined action space?)\n\n## Notes\n\n* I think that the naturalness of activities might also come from the fact that desires are satisfied for quite some time giving the agent the opportunity to concentrate on more diverse activities than in the baselines.\n\n## Suggestions\n\n* I believe this paper should cite Park et. al 2023 (https://arxiv.org/abs/2304.03442), a seminal work in human behavior simulation, and Colas et. al. 2023, which presents an intrinsically-motivated agent operating on other principles than Maslow's hierarchy (https://arxiv.org/abs/2305.12487).", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "## Originality\n\nThe paper is quite original, as I have not seen Maslow's hierarchy of needs used in the context of a text agent.\n\n## Quality\n\nThe paper is well-written, experiments are quite well-designed, several seeds are provided to account for variability. The figures are nice, and the main one does a good job of summarizing how the agent works. The results of the paper support the claims made in the introduction and the abstract.\n\n## Clarity\n\nThe paper was easy to follow and the points are clear and easy to grasp.\n\n## Significance\n\nThe paper demonstrates a recipe for creating more realistic human daily trajectory activities. I can see the type of agent developed here being useful for other applications, such as creating LLM-based non-playable characters in video games.", "weaknesses": "* I am not completely convinced of the end-goal of this paper, specifically, building sequences of human activities. I see the authors justifying this goal in the potential for generating data for psychological, economic or sociological academic study. However, the validity of the generated behavior with respect to at least one downstream application is not investigated in the paper. How to make sure the data generated is useful in these contexts?\n* The introduction also briefly argues that building agents with behaviors aligning with human ones will guarantee their intelligence (the Turing test argument). But unfortunately this does not seem to be the case; I cannot see how giving agents simulated desires will make them score higher on GSM8K for instance.\n* A human-judge evaluation of the validity of the AI judge would be nice (although I am still pretty convinced of the comparison results)\n* I think there is a methodological flaw in the design of the human reference agent: the human is (as far as I understood) given a list of the desires, and the criteria is whether the agents are able to minimize dissatisfaction for those same desires. A better reference would be a dataset of real human activities on a stay-at-home day;", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "C7ZHa1G3ci", "reviewer": "Reviewer_UXrD", "summary": "The paper introduces a Desire-driven Autonomy framework for LLM-based agents to simulate human-like daily activities. The framework is inspired by Maslow's theory of needs and includes 11 dimensions of human-like desires. The Desire-driven Autonomous Agent (D2A) operates based on intrinsic motivations, autonomously proposing and selecting tasks that fulfill its motivational framework. The authors developed a flexible text-based activity simulator using Concordia components, supporting various agent types and textual environments for reliable interaction and evaluation. They conducted simulations in a detailed textual home environment with a Game Master providing relevant observations. The experiments demonstrated that D2A generates appropriate activities effectively and efficiently, achieving described desires. A comparative analysis with three baseline approaches (ReAct, BabyAGI, and LLMob) showed that D2A generates more natural, coherent, and plausible daily activities.", "questions": "See the Weaknesses.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "- The proposed framework allows agents to operate based on intrinsic motivations, which is a significant departure from existing task-oriented AI agents that rely on explicit instructions or external rewards. \n- The paper is well written and has nice figures.\n- The authors conducted a comprehensive comparative analysis with three baseline approaches (ReAct, BabyAGI, and LLMob) to evaluate the effectiveness of their framework. The development of a flexible text-based activity simulator using Concordia components is another strength of the paper.", "weaknesses": "I think there are some weaknesses in this paper:\n- Limited Technological Innovation. The paper primarily focuses on the conceptual framework and theoretical underpinnings of the desire-driven autonomy approach. While the idea of using intrinsic motivations inspired by Maslow's theory of needs is innovative, the technological implementation details might not be as groundbreaking or novel within the recent advancements in the field of AI and LLMs. The work seems in the flow of LLM agents, while I think it is more of a LLM project rather than a technologically-solid paper.\n- Although the paper demonstrates the effectiveness of the D2A agent in a specific textual home environment, there might be questions about its generalization and scalability to other environments or domains. \n- The authors have established a set of concepts, such as human needs, desires, characteristics, and values, to guide the model's behavior. Although I understand the authors' intent to direct the generation of human-like daily activities, I am uncertain about the definition and composition of these intermediate variables. They are determined by the authors without sufficient psychological support or experimental validation to substantiate the overall design. How is the overall pipeline designed? I.e., for the five-level Maslow model, why you further define 11 desire dimensions?\n- The experiment is limited. The testing environment is confined to a single room containing a kitchen, living area, bedroom, and bathroom. It is unclear whether there is any randomness or variation between each epoch's setup, such as the rooms in the house or the items within the room. The experiments should be conducted in a wider variety of settings to minimize the impact of environmental bias and to get general ideas. How many different scenes or settings are included in the experiment? Also, for LLMs, have the authors studied how the prompt design will influence the overall results?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "okk98P200G", "reviewer": "Reviewer_dH5e", "summary": "The paper introduces the Desire-driven Autonomous Agent (D2A), a framework for simulating human-like daily activities based on intrinsic motivations rather than explicit tasks or external rewards. Inspired by Maslow's Theory of Needs, D2A prioritizes actions that fulfill a hierarchy of desires (e.g., physiological, social, self-actualization), allowing it to autonomously select actions that align with its motivational framework. This desire-driven approach contrasts with traditional task-oriented agents by focusing on fulfilling internal motivations, which provides the agent with the capacity for more adaptable and human-like behavior.", "questions": "Please answer the issues mentioned in the weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**1. Proactive Action Based on Intrinsic Motivation**: The proposed D2A framework demonstrates an ability to proactively initiate actions driven by intrinsic motivations. Through the integration of a value system and a desire-driven planner, the framework establishes a dynamic interaction between desires and actions, wherein lowered desire values trigger corresponding activities to restore balance. This mechanism, though relatively simple, allows the agent to engage autonomously in daily activities in a manner that mirrors proactive human behavior, setting it apart from purely reactive or task-driven models.\n\n**2. Human-Inspired Intrinsic Motivations Across Life Dimensions**: Unlike recent approaches that focus on intrinsic motivations for exploration or collaboration, the D2A framework offers a multi-dimensional model inspired by human needs. By integrating eleven desire dimensions (e.g., physiological, social, and self-fulfillment needs), D2A provides a broader, more human-like motivational structure. This approach goes beyond typical reward-driven or exploratory motivations by simulating daily life activities that dynamically balance internal desires, reflecting human motivation patterns more authentically. This novelty enhances the agent's potential for replicating realistic, human-inspired behaviors within single-agent environments.", "weaknesses": "**1. Inconsistent Application of Maslow’s Theory**: While D2A is presented as being inspired by Maslow’s Theory of Needs, it does not implement the theory’s hierarchical structure. According to Maslow, higher-level desires are pursued only once lower-level needs are met, but D2A treats each desire independently, allowing the agent to pursue higher-level needs without satisfying foundational ones. This weakens the theoretical foundation and could make the agent's behavior feel less authentically human-like.\n\n**2. Overly Simplistic Desire-Action Dynamics**: Despite its innovative multi-dimensional motivational structure, the D2A framework uses a straightforward linear deduction of desire values to simulate the fluctuation of needs. This approach falls short of capturing the organic variation in human desires, which often intensify or wane in response to context, time, or recent actions. By exploring more complex dynamics—such as non-linear decay, situational adjustments, or time-of-day cycles—the framework could better reflect realistic interactions between desires and actions. The current simplicity reduces the authenticity of the agent's behavior, limiting the depth of its desire-driven model.\n\n**3. Narrow Focus on Physiological Desires in Single-Agent Setting**: The experimental results in the paper primarily emphasize physiological desires, with minimal exploration of higher-level motivations such as social connectivity or self-actualization. Additionally, the study tests only one agent, limiting insights into potential interactions or complex social behaviors that might arise in multi-agent settings. These restrictions make the results narrowly focused and reduce the paper's ability to demonstrate the full range of behaviors that D2A could potentially simulate, ultimately constraining the framework’s demonstrated impact.\n\n**4. Opaque GPT-4o Evaluation Methodology**: The evaluation of human-likeness using GPT-4o lacks transparency. Key details—such as the prompts used, scoring consistency, and validation of the assessment criteria—are not provided, making it difficult to gauge the robustness of the results. This lack of methodological clarity limits confidence in whether the evaluation effectively captures nuanced human-like behavior or merely reflects surface-level patterns.\n\n**5. Limited Qualitative Evaluation of Generated Action Sequences**: The paper lacks an in-depth qualitative analysis of the action sequences generated by D2A, making it difficult to assess the framework’s true effectiveness. The brief, simplistic sequence shown in Appendix O does not illustrate the model’s potential for creating complex, dynamic routines, leaving the impact of D2A’s design unclear. More detailed, varied sample sequences, along with thoughtful discussion, would provide stronger evidence of D2A's capabilities in simulating realistic human-like behaviors.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)