GenAI4ELab/papercli-papers huggingface dataset:
A Deployed Online Reinforcement Learning Algorithm in an Oral Health Clinical Trial
Authors: Anna L. Trella; Kelly W. Zhang; Hinal Jajal; Inbal Nahum-Shani; Vivek Shetty; Finale Doshi-Velez; Susan A. Murphy
Venue: AAAI 2025 (main)

Abstract: Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recommendations for twice-daily tooth brushing, adherence to recommended oral self-care behaviors remains sub-optimal due to factors such as forgetfulness and disengagement. To address this, we developed Oralytics, a mHealth intervention system designed to complement clinician-delivered preventative care for marginalized individuals at risk for dental disease. Oralytics incorporates an online reinforcement learning algorithm to determine optimal times to deliver intervention prompts that encourage oral self-care behaviors. We have deployed Oralytics in a registered clinical trial. The deployment required careful design to manage challenges specific to the clinical trials setting in the U.S. In this paper, we (1) highlight key design decisions of the RL algorithm that address these challenges and (2) conduct a re-sampling analysis to evaluate algorithm design decisions. A second phase (randomized control trial) of Oralytics is planned to start in spring 2025.
AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction
Authors: Tianlong Xu; YiFan Zhang; Zhendong Chu; Shen Wang; Qingsong Wen
Venue: AAAI 2025 (main)

Abstract: Students frequently make mistakes while solving mathematical problems, and traditional error correction methods are both time-consuming and labor-intensive. This paper introduces an innovative Virtual AI Teacher system designed to autonomously analyze and correct student Errors (VATE). Leveraging advanced large language models (LLMs) like GPT-4, the system uses student drafts as a primary source for error analysis, which enhances understanding of the student's learning process. It incorporates sophisticated prompt engineering and maintains an error pool to reduce computational overhead. The AI-driven system also features a real-time dialogue component for efficient student interaction. Our approach demonstrates significant advantages over traditional and machine learning-based error correction methods, including reduced educational costs, high scalability, and superior generalizability. The system has been deployed in Squirrel AI's learning platform for elementary mathematics education, where it achieves 78.3% accuracy in error analysis and shows a marked improvement in student learning efficiency. Satisfaction surveys indicate a strong positive reception, highlighting the system's potential to transform educational practices.
IMQC: A Large Language Model Platform for Medical Quality Control
Authors: Qi Ye; Guangya Yu; Jingping Liu; Erzhen Chen; Chenjie Dong; Xiaosheng Lin; Zelei Liu; Han Yu; Tong Ruan
Venue: AAAI 2025 (main)

Abstract: Medical quality control (MQC) indicators are essential for evaluating the performance of healthcare institutions to ensure high-quality patient care. In this paper, we report the design, implementation, and deployment of the Intelligent EMR-LLM platform for Medical Quality Control (IMQC), a large language model (LLM)-empowered system for automatically computing MQC indicators for enhancing the quality of medical services in Shanghai. It consists of an LLM (i.e., EMR-LLM) for processing electronic medical records (EMRs). With EMR-LLM, IMQC translates existing MQC indicators into a standardized representation language and automatically computes them based on EMRs. Since its deployment in February 2024, IMQC has been adopted by the Shanghai Medical Quality Management Center and associated hospitals. So far, it has processed 1,245 medical quality indicators for secondary- and tertiary-level hospitals, achieving an MQC evaluation accuracy of 93.31%, which is comparable to human experts. It has significantly improved efficiency, increasing from 10 EMRs per hour per human expert to over 1,000 EMRs per hour on average using one single H800 GPU. Over the first round of deployment in Shanghai, it is estimated that IMQC saves around 3.42 million RMB per month in manpower costs compared to traditional reporting methods. The successful deployment of IMQC sets a precedence for other regions to adopt similar AI-driven solutions to enhance medical quality control.
XCotton: Advancing AI-Enabled Hardware/Software Integrated System for Foreign Fiber Cleaning
Authors: Gaowei Zhang; Wei Wang; Yusheng Peng; Yi Wang
Venue: AAAI 2025 (main)

Abstract: Cotton is a critical agricultural product and industrial raw material, playing a key role in the national economies and people's living conditions, particularly in developing countries. However, cotton picking and processing often result in the contamination with various foreign fibers, such as hair, hemp rope, plastic film, and polypropylene rope. These contaminants are difficult to remove during textile processing and tend to break into small fragments, significantly reducing the quality of cotton products and negatively impacting the cotton industry. In this paper, we present an AI-enabled hardware-software integrated system--XCotton, for identifying and removing foreign fibers. Our system has been deployed in actual cotton production environments in the multiple regions in China, Central Asia, and Africa. XCotton achieves a cleaning efficiency of 1000kg/h, representing a 43% improvement, with only 14 kWh energy consumption (63% less). Moreover, XCotton brings significant business values to its manufacturer and clients. XCotton not only enhances the quality of cotton products but also contributes to the value-adding and upgrading of the cotton industry in developing regions, supporting economic growth and improving living conditions.
ScriptSmith: A Unified LLM Framework for Enhancing IT Operations via Automated Bash Script Generation, Assessment, and Refinement
Authors: Pooja Aggarwal; Oishik Chatterjee; Ting Dai; Suranjana Samanta; Prateeti Mohapatra; Debanjana Kar; Ruchi Mahindru; Steve Barbier; Eugen Postea; Brad Blancett; Arthur de Magalhaes
Venue: AAAI 2025 (main)

Abstract: In the rapidly evolving landscape of site reliability engineering (SRE), the demand for efficient and effective solutions to manage and resolve issues in site and cloud applications is paramount. This paper presents an innovative approach to action automation using large language models (LLMs) for script generation, assessment, and refinement. By leveraging the capabilities of LLMs, we aim to significantly reduce the human effort involved in writing and debugging scripts, thereby enhancing the productivity of SRE teams. Our experiments focus on Bash scripts, a commonly used tool in SRE, and involve the CodeSift dataset of 100 tasks and the InterCode dataset of 153 tasks. The results show that LLMs can automatically assess and refine scripts efficiently,  reducing the need for script validation in an execution environment. Results demonstrate that the framework shows an overall improvement of 7-10% in script generation.
GCF: Estimating Unobserved Demand Using Graph Causal Forecasting
Authors: Sayantan Basu; Mohit Kumar; Sivaramakrishnan Kaveri
Venue: AAAI 2025 (main)

Abstract: Time series data, prevalent in fields like medical, e-commerce, finance, etc., is used for forecasting, such as predicting next quarter’s product demand based on past trends. However, some problems necessitate causal models to answer questions like “What the product demand would have been without a specific intervention (e.g., products with slower delivery time suppressed from the search results)?” 
Such questions require causal models to estimate unobserved counterfactual outcome. In this paper, we propose a novel Graph Causal Forecasting (GCF) model, that predicts the unobserved demand leveraging the relationship of a product with other similar products in the marketplace (spatial aspect), along with change in demand over time for each product (temporal aspect). The core idea is to estimate the counterfactual outcome using a synthetic control unaffected by the treatment.  Our approach uses RGCN-dilated CNN based network, which leverages domain knowledge to automatically design a synthetic control during training. Using GCF for our demand forecasting problem, we achieve 75.3% lower MAPE compared to baseline. We use the forecasted values to recommend high demand products, in terms of our business metric (discussed later) which tracks the quality of these recommendations, we achieve a significant jump of 61.2%. Moreover, it adds 67.8% more high demand products to the marketplace, compared to existing model in production.
Deployment of GCF in 2023, led to +1399 bps improvement in number of products with a view from customers, and +310 bps improvement in number of products with a sale. We also compare GCF with state of the art forecasting methods on a semi-synthetic data, created by simulating a treatment on open source traffic data METR-LA. We achieve 30% lower MSE against TGCN, a time series forecasting approach and  30% lower MSE against CRN and 25% lower MSE against Google Causal Impact model, both of which are causal forecasting approaches.
Optimizing Vital Sign Monitoring in Resource-Constrained Maternal Care: An RL-Based Restless Bandit Approach
Authors: Niclas Boehmer; Yunfan Zhao; Guojun Xiong; Paula Rodriguez-Diaz; Paola Del Cueto  Cibrian; Joseph Ngonzi; Adeline Boatin; Milind Tambe
Venue: AAAI 2025 (main)

Abstract: Maternal mortality remains a significant global public health challenge. One promising approach to reducing maternal deaths occurring during facility-based childbirth is through early warning systems, which require the consistent monitoring of mothers' vital signs after giving birth. Wireless vital sign monitoring devices offer a labor-efficient solution for continuous monitoring, but their scarcity raises the critical question of how to allocate them most effectively. We devise an allocation algorithm for this problem by modeling it as a variant of the popular Restless Multi-Armed Bandit (RMAB) paradigm. In doing so, we identify and address novel, previously unstudied constraints unique to this domain, which render previous approaches for RMABs unsuitable and significantly increase the complexity of the learning and planning problem. To overcome these challenges, we adopt the popular Proximal Policy Optimization (PPO) algorithm from reinforcement learning to learn an allocation policy by training a policy and value function network. We demonstrate in simulations that our approach outperforms the best heuristic baseline by up to a factor of 4.
Structured Document Generation for Industrial Equipment
Authors: Karol Lynch; Fabio Lorenzi; John D Sheehan; Duygu Kabakci-Zorlu; Bradley Eck
Venue: AAAI 2025 (main)

Abstract: We describe an application that uses large language models to generate structured documents related to industrial equipment, specifically focusing on Failure Modes and Effects Analysis (FMEAs). Our novel application uses techniques in structured document generation, in-context learning, and ensembling to create high-quality structured content that subject matter experts supervise through a user-centric interface that presents FMEA entities as UI elements. Novel evaluation metrics for structured document generation are also proposed. Our empirical results, based on 71 asset evaluations, demonstrate the individual and combined contributions of these techniques, with an overall effectiveness that varies between a recall of 0.669 and a precision of 0.91. Qualitative feedback from target users validates the practicality of the described approach to seamlessly integrate expert supervision with generative AI in a labour-saving workflow.
Hotspotter: A Generalizable Pipeline for Automated Detection of Subtle Volcanic Thermal Features in Satellite Images
Authors: Aditya Mohan; Andie Gomez-Patron; Matthew Pritchard; Hannah Kerner
Venue: AAAI 2025 (main)

Abstract: Geologists seek to understand the relationship between volcanic unrest and eruptions by identifying subtle Volcanic Thermal Features (VTFs) in high-resolution satellite imagery. This analysis requires the careful curation of large databases of relevant volcanic thermal information. However, volcanic unrest is characterized by highly subtle thermal anomalies. Manual identification on a global scale is highly labor- and time-intensive. We propose Hotspotter: an end-to-end system to automatically detect subtle volcanic thermal anomalies in satellite images and derive relevant thermal statistics. Previous solutions for automated VTF detection have limited data size and geographic diversity. To accommodate an unprecedentedly large and diverse volcanic dataset, we propose an automated pipeline combining unsupervised anomaly detection with supervised classification to filter anomalous regions. Hotspotter gives 90% anomaly detection accuracy and robust generalization to new volcanoes. Our automated approach can accelerate scientists' search for VTFs to help identify relevant thermal precursors and enable more precise forecasts of global volcanic eruptions.
ECLAIR: Enhanced Clarification for Interactive Responses
Authors: John Murzaku; Zifan Liu; Md Mehrab Tanjim; Vaishnavi Muppala; Xiang Chen; Yunyao Li
Venue: AAAI 2025 (main)

Abstract: We present ECLAIR (Enhanced CLArification for Interactive Responses), a novel unified and end-to-end framework for interactive disambiguation in enterprise AI assistants. ECLAIR generates clarification questions for ambiguous user queries and resolves ambiguity based on the user's response. We introduce a generalized architecture capable of integrating ambiguity information from multiple downstream agents, enhancing context-awareness in resolving ambiguities and allowing enterprise specific definition of agents. We further define agents within our system that provide domain-specific grounding information. We conduct experiments comparing ECLAIR to few-shot prompting techniques and demonstrate ECLAIR's superior performance in clarification question generation and ambiguity resolution.
SNAP: Semantic Stories for Next Activity Prediction
Authors: Alon Oved; Segev Shlomov; Sergey Zeltyn; Nir Mashkif; Avi Yaeli
Venue: AAAI 2025 (main)

Abstract: Predicting the next activity in an ongoing process is one of the most common tasks in the business process management (BPM) domain. It allows businesses to optimize resource allocation, enhance operational efficiency, and aid both in risk mitigation and strategic decision-making. Existing state-of-the-art AI models for BPM do not fully capitalize on available semantic information within process event logs. As current advanced AI-BPM systems provide semantically richer textual data, the need for new adequate models grows. 
To address this gap, we develop SNAP—a novel system that utilizes LLMs by constructing narratives and semantic contextual stories for historical event logs, which are then used to generate precise and actionable predictions for the ongoing process.
SNAP was evaluated on six benchmark datasets, where it demonstrated significant performance improvements over eleven SOTA models, particularly on datasets with high levels of semantic content. 
This work showcases the potential of integrating LLMs in BPM and outlines a clear path toward future deployment, emphasizing the relevance and innovation of our approach within the broader AI application landscape.
An Application-Agnostic Automatic Target Recognition System Using Vision Language Models
Authors: Anthony Palladino; Dana Gajewski; Abigail Aronica; Patryk Deptula; Alexander Hamme; Seiyoung C. Lee; Jeff Muri; Todd Nelling; Michael A. Riley; Brian Wong; Margaret Duff
Venue: AAAI 2025 (main)

Abstract: We present a novel Automatic Target Recognition (ATR) system using open-vocabulary object detection and classification models. A primary advantage of this approach is that target classes can be defined just before runtime by a non-technical end user, using either a few natural language text descriptions of the target, or a few image exemplars, or both. Nuances in the desired targets can be expressed in natural language, which is useful for unique targets with little or no training data. We also implemented a novel combination of several techniques to improve performance, such as leveraging the additional information in the sequence of overlapping frames to perform tubelet identification (i.e., sequential bounding box matching), bounding box re-scoring, and tubelet linking. Additionally, we developed a technique to visualize the aggregate output of many overlapping frames as a mosaic of the area scanned during the aerial surveillance or reconnaissance, and a kernel density estimate (or heatmap) of the detected targets. We initially applied this ATR system to the use case of detecting and clearing unexploded ordinance on airfield runways and we are currently extending our research to other real-world applications.
Automating the Expansion of Instrument Typicals in Piping and Instrumentation Diagrams (P&IDs)
Authors: Vasil Shteriyanov; Rimma Dzhusupova; Jan Bosch; Helena Holmström Olsson
Venue: AAAI 2025 (main)

Abstract: Within the Engineering, Procurement, and Construction (EPC) industry, engineers manually create documents based on engineering drawings, which can be time-consuming and prone to human error. For example, the expansion of typical assemblies of instrument items (Instrument Typicals) in Piping and Instrumentation Diagrams (P&IDs) is a labor-intensive task. Each Instrument Typical assembly is depicted in the P&IDs via a simplified representation showing only a subset of the utilized instruments. The expansion activity involves recording all utilized instruments to create an instrument item list document based on the P&IDs for a particular EPC project. Fortunately, Artificial Intelligence (AI) could help to automate this process.

In this paper, we propose the first method for automating the process of Instrument Typical expansion in P&IDs. The method utilizes computer vision techniques and domain knowledge rules to extract information about the Instrument Typicals from a project's P&IDs and legend sheets. Subsequently, the extracted information is used to automatically generate the listing of all utilized instruments. The effectiveness of our method is evaluated on P&IDs from large industrial EPC projects, resulting in precision rates exceeding 98% and recall rates surpassing 99%. These results demonstrate the suitability of our method for industrial deployment. The successful application of our method has the potential to reduce engineering costs and increase the efficiency of EPC projects. Furthermore, the method could be adapted for additional applications in the EPC industry, which highlights the method's industrial value.
OnAIR: Applications of the NASA On-Board Artificial Intelligence Research Platform
Authors: Evana Gizzi; Timothy Chase Jr; Christian Cassamajor-Paul; Rachael Chertok; Lily Clough; Connor Firth; Alan Gibson; Ibrahim Haroon; James Marshall; Patrick Maynard; Michael Monaghan; Hayley Owens; Daniel Rogers; Mahmooda Sultana; Jivko Sinapov; Bethany Theiling
Venue: AAAI 2025 (main)

Abstract: Infusing artificial intelligence algorithms into production aerospace systems can be challenging due to costs, timelines, and a risk-averse industry. We introduce the Onboard Artificial Intelligence Research (OnAIR) platform, an open-source software pipeline and cognitive architecture tool that enables full life cycle AI research for on-board intelligent systems. We begin with a description and user walk-through of the OnAIR tool. Next, we describe four use cases of OnAIR for both research and deployed onboard applications, detailing their use of OnAIR and the benefits it provided to the development and function of each respective scenario. We conclude with remarks on future work, future planned deployments, and goals for the forward progression of OnAIR as a tool to enable a larger AI and aerospace research community.
BoolXAI: Explainable AI Using Expressive Boolean Formulas
Authors: Serdar Kadioglu; Elton Yechao Zhu; Gili Rosenberg; John Kyle Brubaker; Martin J. A. Schuetz; Grant Salton; Zhihuai Zhu; Helmut G. Katzgraber
Venue: AAAI 2025 (main)

Abstract: In this tool paper, we design, develop, and release BoolXAI, an interpretable machine learning classification approach for Explainable AI (XAI) based on expressive Boolean formulas. The Boolean formula defines a logical rule with tunable complexity according to which input data are classified. Beyond the classical conjunction and disjunction, BoolXAI offers expressive operators such as AtLeast, AtMost, and Choose and their parameterization. This provides higher expressiveness compared to rigid rules- and tree-based approaches. We show how to train BoolXAI classifiers effectively using native local optimization to search the space of feasible formulas. We provide illustrative results on several well-known public benchmarks that demonstrate the competitive nature of our approach compared to existing methods. Our work is embodied in the open-source BoolXAI library with a high-level user interface to serve researchers and practitioners. BoolXAI can be used either as a standalone interpretable classifier or for post-hoc explanations of other black-box models or observed behavior. We highlight several desirable benefits of our tool, especially in industrial settings where rapid experimentation, reusability, reproducibility, deployment, and maintenance are of great interest. Finally, we showcase a deployed service powered by BoolXAI as an enterprise application.
Exploring the Efficacy of Multi-Agent Reinforcement Learning for Autonomous Cyber Defence: A CAGE Challenge 4 Perspective
Authors: Mitchell Kiely; Metin Ahiskali; Etienne Borde; Benjamin Bowman; David Bowman; Dirk van Bruggen; KC Cowan; Prithviraj Dasgupta; Erich Devendorf; Ben Edwards; Alex Fitts; Sunny Fugate; Ryan Gabrys; Wayne Gould; H. Howie Huang; Jules Jacobs; Ryan Kerr; Isaiah J. King; Li Li; Luis Martinez; Christopher Moir; Craig Murphy; Olivia Naish; Claire Owens; Miranda Purchase; Ahmad Ridley; Adrian Taylor; Sara Farmer; William John Valentine; Yiyi Zhang
Venue: AAAI 2025 (main)

Abstract: As cyber threats become increasingly automated and sophisticated, novel solutions must be introduced to improve defence of enterprise networks. Deep Reinforcement Learning (DRL) has demonstrated potential in mitigating these advanced threats. Single DRL Agents have proven utility toward execution of autonomous cyber defence. Despite the success of employing single DRL Agents, this approach presents significant limitations, especially regarding scalability within large enterprise networks. An attractive alternative to the single agent approach is the use of Multi-Agent Reinforcement Learning (MARL). However, developing MARL agents is costly with few options for examining MARL cyber defence techniques against adversarial agents. This paper presents a MARL network security environment, the fourth iteration of the Cyber Autonomy Gym for Experimentation (CAGE) challenges. This challenge was specifically designed to test the efficacy of MARL algorithms in an enterprise network. Our work aims to evaluate the potential of MARL as a robust and scalable solution for autonomous network defence.