United States

Infectious diseases have historically had profound effects on global health, economies, and social structures. Effective tracing of infectious diseases is essential not only for immediate public health responses but also for shaping future prevention strategies. Traditional tracing methods often emphasize homogeneous networks, overlooking the diverse transmission characteristics of heterogeneous populations. This research addresses two critical challenges: the heterogeneity of transmission across various media and modes, and the significant yet underexplored influence of community structures on epidemic spread and tracing.We propose a Heterogeneous Hypergraph Attention Network (HHAN) modelthat accounts for multiple transmission pathways and patterns within heterogeneous networks.  HHAN integrates a heterogeneous graph neural network module to handle the complexity of communication among different populations, and an Agent-Based Modeling Module that combines agent-based ideas to model individual behaviors. This approach effectively captures complex interactions within community structures and addresses individual variability. Experimental results on three real-world datasets demonstrate that the HHAN model significantly outperforms other state-of-the-art methods in tackling the complex challenge of tracing infectious diseases in heterogeneous populations.

AAAI 2025

HHAN: Comprehensive Infectious Disease Source Tracing via Heterogeneous Hypergraph Neural Network

social networks

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).

A ticket is required to attend this event, please register using the link below:

https://aamas2022-conference.auckland.ac.nz/attending/registration/

Registration Is Required

AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Motion planning is a crucial component in autonomous vehicles. State-of-the-art motion planners are trained on meticulously curated datasets, which are not only expensive to annotate but also insufficiently containing rarely-seen critical scenarios. Failing to capture such scenarios inevitably risks motion planners and potentially causes incidents during testing. One intuitive solution is to compose such scenarios by programming and executing a simulator (e.g., CARLA). However, this requires huge human cost. I'm motivated to develop a cheap method to generate diverse critical traffic scenarios for training better motion planners. First, I represent traffic scenarios by scripts, using which the simulator produces traffic scenarios. Then, I develop a method which accepts user-specified text description, which a Large Language Model (LLM) translates into scripts using in-context learning.
The output scripts are sent to the simulator which produces the corresponding traffic scenarios. As my method can generate abundant safety-critical traffic scenarios, I use them as synthetic training data for motion planners. To demonstrate the value of the generated scenarios, I train existing motion planners on my synthetic data, existing real-world datasets, and the mix of my data with the existing datasets.
My experiments show that motion planners trained with my data significantly outperforms the counterparts trained only on real-world data, showing the usefulness of my synthetic data and the effectiveness of my data generation method.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner

Weakly-Supervised Dense Video Captioning (WSDVC) aims to localize and describe all events of interest in a video without requiring annotations of event boundaries. This setting poses a great challenge in accurately locating the temporal location of event, as the relevant supervision is unavailable. Existing methods rely on explicit alignment constraints between event locations and captions, which involve complex event proposal procedures during both training and inference. To tackle this problem, we propose a novel implicit location-caption alignment paradigm by complementary masking, which simplifies the complex event proposal and localization process while maintaining effectiveness. Specifically, our model comprises two components: a dual-mode video captioning module and a mask generation module. The dual-mode video captioning module captures global event information and generates descriptive captions,
while the mask generation module generates differentiable positive and negative masks for localizing the events. These masks enable the implicit alignment of event locations and captions by ensuring that captions generated from positively and negatively masked videos are complementary, thereby forming a complete video description. In this way, even under weak supervision, the event location and event caption can be aligned implicitly. Extensive experiments on the public datasets demonstrate that our method outperforms existing weakly-supervised methods and achieves competitive results compared to fully-supervised methods. Code will be released.

Implicit Location-Caption Alignment via Complementary Masking for Weakly-Supervised Dense Video Captioning

The field of music generation has seen a surge of interest from both academia and industry, with innovative platforms such as Suno, Udio, and SkyMusic earning widespread recognition. However, the challenge of music infilling—modifying specific music segments without reconstructing the entire piece—remains a significant hurdle for both audio-based and symbolic-based models, limiting their adaptability and practicality. In this paper, we address symbolic music infilling by introducing the Collaborative Music Inpainter (CMI), an advanced human-in-the-loop (HITL) model for music infilling. The CMI features the Joint Embedding Predictive Autoregressive Generative Architecture (JEP-AGA), which learns the high-level predictive representations of the masked part that needs to be infilled during the autoregressive generative process, akin to how humans perceive and interpret music. The newly developed Dynamic Interaction Learner (DIL) achieves HITL by iteratively refining the infilled output based on user interactions alone, significantly reducing the interaction cost without requiring further input. Experimental results confirm CMI’s superior performance in music infilling, demonstrating its efficiency in producing high-quality music.

Compose with Me: Collaborative Music Inpainter for Symbolic Music Infilling

Knowledge compilation is a method of transforming knowledge into a compressed and tractable form, permitting more efficient operations. For Boolean functions, numerous representations have been proposed to enhance succinctness and tractability. In this paper, we introduce a new representation named structured Decomposable And-Sum Circuit (st-DASC), which employs $\wedge$ and $+$ nodes with signed edges, in place of the standard $\wedge$ and $\vee$ nodes with unsigned edges. Notably, incorporating negative signs permits polytime logical negation. By following the knowledge compilation map, we show that st-DASC is more succinct than the Sentential Decision Diagram (SDD) while still maintaining support for all of the operations on the knowledge compilation map that SDD supports. Furthermore, st-DASC is even more succinct than structured d-DNNF (st-d-DNNF), which itself is more succinct but supports fewer operations than SDD. Accordingly, st-DASC breaks the traditional trade-off between succinctness and tractability.

An and-Sum Circuit with Signed Edges That Is More Succinct than SDD

This paper presents VDAct, a dataset for a Video-grounded Dialogue on Event-driven Activities, alongside VDEval, a session-based context evaluation metric specially designed for the task. Unlike existing datasets, VDAct includes longer and more complex video sequences that depict a variety of event-driven activities that require advanced contextual understanding for accurate response generation. The dataset comprises 3,000 dialogues with over 30,000 question-and-answer pairs, derived from 1,000 videos with diverse activity scenarios. VDAct displays a notably challenging characteristic due to its broad spectrum of activity scenarios and wide range of question types. Empirical studies on state-of-the-art vision foundation models highlight their limitations in addressing certain question types on our dataset. Furthermore, VDEval, which integrates dialogue session history and video content summaries extracted from our supplementary KGs to evaluate individual responses, demonstrates a significantly higher correlation with human assessments on the VDAct dataset than existing evaluation metrics that rely solely on the context of single dialogue turns.

A Video-grounded Dialogue Dataset and Metric for Event-driven Activities

Deep reinforcement learning (DRL) has gained significant attention in autonomous systems, yet its black-box nature and lack of explainability hinder user trust in safety-critical domains such as autonomous driving. Existing experience replay approaches enhance sample efficiency but often fail to capture the internal causality of training data, leading to a convoluted training process that is difficult for humans to explain. In this work, we introduce Experience Replay with Causal Inference (ERCI), an explainable approach that integrates time series representation and causal inference to offer human-aligned explanations for DRL. Specifically, ERCI 1) introduces a novel multivariate time series representation to extract explainable Time Series Causal Factors (TSCF) from experimental data and 2) leverages internal causality in TSCFs with causal inference as a crucial standard for experience replay in DRL training. We evaluate ERCI using multiple baseline algorithms across diverse environments. Results show that ERCI provides human-aligned explanations and further improves sample efficiency through enhanced explainability. Notably, ERCI outperforms other state-of-the-art approaches by 15% in average performance, highlighting its effectiveness and generalizability.

ERCI: An Explainable Experience Replay Approach with Causal Inference for Deep Reinforcement Learning

This study evaluates the usage of OpenAI’s ChatGPT Large Language Model (LLM) as a tool for constructing multiple choice questions for assessing student academic performance through quizzes and exams. We randomly deploy questions constructed with and without use of the LLM tool and gauge the ability of the students to correctly answer, as well as their ability to correctly perceive the difference between human-authored and LLM-authored questions. In determining whether the questions written with the aid of ChatGPT were consistent with the instructor's questions and source text, we computed representative vectors of both the human and ChatGPT questions using SBERT and compared cosine similarity to the course textbook. A non-significant Mann-Whitney U test ($\textit{z}$ = 1.018, $\textit{p}$ = .309) suggests that students were unable to perceive whether questions were written with or without the aid of ChatGPT. However, student scores on LLM-authored questions were almost 9\% lower ($\textit{z}$ = 2.702, $\textit{p}$ $<$ .01). This result may indicate that either the AI questions were more difficult or that the students were more familiar with the instructor’s style of questions. Overall, the study suggests that while there is potential for using LLM tools to aid in the construction of assessments, care must be taken to ensure that the questions are fair, well-composed, and relevant to the course material.

LLMs in the Classroom: Outcomes and Perceptions of Questions Written with the Aid of AI

This paper addresses the problem of preference learning, which aims to align robot behaviors through learning user-specific preferences (e.g., “good pull-over location”) from visual demonstrations. Despite its similarity to learning factual concepts (e.g., “red cube”), preference learning is a fundamentally harder problem due to its subjective nature and the paucity of person-specific training data. We address this problem using a novel framework called Synapse, which is a neuro-symbolic approach designed to efficiently learn preferential concepts from limited data. Synapse represents preferences as neuro-symbolic programs -- facilitating inspection of individual parts for alignment -- in a domain-specific language (DSL) that operates over images and leverages a novel combination of visual parsing, large language models, and program synthesis to learn programs representing individual preferences. We perform extensive evaluations on various preferential concepts as well as a small user case study demonstrating the ability of Synapse to align well with dissimilar user preferences. Our method significantly outperforms baselines, especially in out-of-distribution generalization. We show the importance of the design choices in the framework through multiple ablation studies.

SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine

Researchers, policymakers, and developers of artificial intelligence (AI) are actively collaborating to establish trustworthy AI standards that align with broader societal values, particularly in the context of large language models (LLMs). However, the critical discourse on bridging the vast knowledge gap between experts who shape and implement standards for LLMs and end-users whose values are at stake remains largely unaddressed. Taking a ‘bottom-up’ approach and using a mixed-method approach, we first conducted interviews (N = 12) to engage with end-users’ perceptions of normative standards in the context of LLMs. We thereby identified 68 criteria that end-users’ consider when evaluating value congruence. Second, we conducted an online survey (N = 379) to further investigate how end-users prioritise these standards and the identified criteria in conversational LLM-based applications. Findings reveal a clear preference for governance, monitoring, and transparent information provision over other criteria and highlight the necessity of non-technical complements to technical solutions for bridging the knowledge gap. We discuss end-user information requirements and outline actionable steps to effectively communicate trustworthy AI standards.

Bridging the Knowledge Gap: Understanding User Expectations for Trustworthy LLM Standards

Despite significant progress has been made in image deraining, most existing methods are limited to handling only a single type of rain degradation or a specific pattern of rain. However, real-world rain scenarios tend to contain diverse rainy patterns due to variations in the rainfall process and lighting conditions. To address this dilemma and advance this field, we introduce a new task: Universal Rainy Image Restoration (URIR), which aims to handle multiple types of rain degradation on a single model. To benchmark this task, we construct a high-quality dataset called URIR-8K, which contains four patterns: rain streak, raindrop, rain accumulation and nighttime rain. Building upon this dataset, we present a comprehensive study on existing approaches by evaluating their universal deraining capabilities and their effect on downstream object detection task. In addition, we design a multi-scale vision Mamba as a baseline model, leveraging the benefits of multi-scale learning for its robustness to diverse rain appearances. Unlike existing methods that use fixed-scale scanning for feature extraction, we employ a multi-scale 2D scanning technique to better help image restoration in the richer scale space. Extensive experimental analysis demonstrates the potential of our proposed task and the effectiveness of the baseline model. The code and dataset will be available.

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Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner

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