United States

In Tennenholtz’s program equilibrium, players of a game submit programs to play on their behalf. Each program receives the other programs’ source code and outputs an action. This can model interactions involving AI agents, mutually transparent institutions, or commitments. (Tennenholtz 2004) proves a folk theorem for program games, but the equilibria constructed are very brittle. We therefore consider simulation-based programs – i.e., programs that work by running opponents’ programs. These are relatively robust (in particular, two programs that act the same are treated the same) and are more practical than proof-based approaches. Oesterheld’s (2019) $\epsilon$Grounded$\pi$Bot is such an approach. Unfortunately, it is not generally applicable to games of three or more players, and only allows for a limited range of equilibria in two player games. In this paper, we propose a generalisation to Oesterheld’s (2019) $\epsilon$Grounded$\pi$Bot. We prove a folk theorem for our programs in a setting with access to a shared source of randomness. We then characterise their equilibria in a setting without shared randomness. Both with and without shared randomness, we achieve a much wider range of equilibria than Oesterheld’s (2019) $\epsilon$Grounded$\pi$Bot. Finally, we explore the limits of simulation-based program equilibrium, showing that the Tennenholtz folk theorem cannot be attained by simulation-based programs without access to shared randomness.

AAAI 2025

Characterising Simulation-Based Program Equilibria

gtep

game theory

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

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.



Coalition formation over graphs is a well studied class of games whose players are vertices and feasible coalitions 
must be connected subgraphs. In this setting, the existence and computation of equilibria, under various notions of stability, has attracted a lot of attention. However, the natural process by which players, starting from any feasible state, strive to reach an equilibrium after a series of unilateral improving deviations, has been less studied. We investigate the convergence of dynamics towards individually stable outcomes under the following perspective: what are the most general classes of preferences and graph topologies guaranteeing convergence? To this aim, on the one hand, we cover a hierarchy of preferences, ranging from the most general to a subcase of additively separable preferences, including individually rational and monotone cases. On the other hand, given that convergence may fail in graphs admitting a cycle even in our most restrictive preference class, we analyze acyclic graph topologies such as trees, paths, and stars.

Individually Stable Dynamics in Coalition Formation over Graphs

Formulating a real-world problem under the Reinforcement
Learning framework involves non-trivial design choices, such
as selecting a discount factor for the learning objective (dis-
counted cumulative rewards), which articulates the planning
horizon of the agent. This work investigates the impact of the
discount factor on the bias-variance trade-off given structural
parameters of the underlying Markov Decision Process. Our
results support the idea that a shorter planning horizon might
be beneficial, especially under partial observability.

On Shallow Planning Under Partial Observability

Personalized federated learning (PFL) has recently gained significant attention for its capability to address the poor convergence performance on highly heterogeneous data and the lack of personalized solutions of traditional federated learning (FL). Existing mainstream approaches either perform personalized aggregation based on a specific model architecture to leverage global knowledge or achieve personalization by exploiting client similarities. However, the former overlooks the discrepancies in client data distributions by indiscriminately aggregating all clients, while the latter lacks fine-grained collaboration of classifiers relevant to local tasks. In view of this challenge, we propose a Personalized Federated learning method for Enhancing Collaboration among Similar Classifiers (PFedCS), which aims at improving the client’s accuracy on local tasks. Concretely, it is achieved by leveraging awareness of the client classifier similarities to address the above problems. By iteratively measuring the distance of the classifier parameters between clients and clustering with each client as a cluster center, the central server adaptively identifies the collaborating clients with similar data distributions. In addition, a distance-constrained aggregation method is designed to generate customized collaborative classifiers to guide local training. As a result, extensive experimental evaluations conducted on three datasets demonstrate that our method achieves state-of-the-art performance.

PFedCS: A Personalized Federated Learning Method for Enhancing Collaboration among Similar Classifiers

Classical planning asks for a sequence of operators reaching a given goal. While the most common case is to compute a plan, many scenarios require more than that. However, quantitative reasoning on the plan space remains mostly unexplored. A fundamental problem is to count plans, which relates to the conditional probability on the plan space. Indeed, qualitative and quantitative approaches are well-established in various other areas of automated reasoning. 

We present the first study to quantitative and qualitative reasoning on the plan space. In particular, we focus on polynomially bounded plans. On the theoretical side, we study its complexity, which gives rise to rich reasoning modes. Since counting is hard in general, we introduce the easier notion of facets, which enables understanding the significance of operators. On the practical side, we implement quantitative reasoning for planning. Thereby, we transform a planning task into a propositional formula and use knowledge compilation to count different plans. This framework scales well to large plan spaces, while enabling rich reasoning capabilities such as learning pruning functions and explainable planning.

Counting and Reasoning with Plans

Trajectory-user linking (TUL) aims to match anonymous trajectories to the most likely users who generated them, offering benefits for a wide range of real-world spatio-temporal applications. However, existing TUL methods are limited by high model complexity and poor learning of the effective representations of trajectories, rendering them ineffective in handling large-scale user trajectory data.In this work, we propose a novel Scalable Trajectory-User Linking with dual-stream representation networks for large-scale TUL problem, named ScaleTUL Specifically, ScaleTUL generates two views using temporal and spatial augmentations to exploit supervised contrastive learning framework to effectively capture the irregularities of trajectories. In each view, a dual-stream trajectory encoder consisting of a long-term encoder and a short-term encoder is designed to learn the unified representations of trajectories that fuses different temporal-spatial dependencies. Then, a TUL layer is used to associate the trajectories with the corresponding users in the representation space using a two-stage training model.Experimental results on check-in mobility datasets from three real-world cities and the nationwide U.S. demonstrate the superiority of ScaleTUL over state-of-the-art baselines for large-scale TUL tasks. The source code of our model is available at https://anonymous.4open.science/r/Code-7EB4.

Scalable Trajectory-User Linking with Dual-Stream Representation Networks

Businesses employ third-party Large Language Models (LLMs), which raises substantial privacy issues due to the risk of exposing sensitive prompts. To mitigate, this paper introduces Portcullis, a privacy-preserving gateway service designed to protect users' sensitive information transparently and reliably, while maintaining the efficiency and accuracy of responses from third-party LLMs.

Portcullis functions as a mediator, anonymizing sensitive data in prompts, securely interacting with the LLM, and accurately reconstructing responses. It ensures all data processing occurs within secure encrypted memory. Additionally, the entire gateway is fully attested to verify its trustworthiness and guarantee that user privacy remains uncompromised. Portcullis is the first of its kind, offering a verifiable and scalable privacy gateway for third-party LLM inferences.


We assess Portcullis's efficiency as a confidential container platform, demonstrating that its startup time scales linearly, ensuring scalability. Additionally, we evaluate its runtime performance using the PII and Enron Email Dataset. For masking and unmasking workloads, Portcullis outperforms Hide-and-Seek by 96x speed up, while maintaining equal or better false positive and false negative rates compared to existing solutions. On the Enron dataset, Portcullis achieves notably higher accuracy, surpassing Hide-and-Seek by over 0.1 for GPT-4o mini.

Portcullis: A Scalable and Verifiable Privacy Gateway for Third-Party LLM Inference

Despite the huge success of text-to-image (TTI) generation models, existing studies seldom consider whether generated images accurately represent factual information. In this paper, we define the problem of image hallucination as the generated images fail to accurately depict factual information. To address this, we introduce I-HallA (Image Hallucination evaluation with Question Answering), an automatic evaluation metric that measures the factuality of generated images through visual question answering (VQA), and I-HallA v1.0, a curated benchmark dataset. We develop a three-stage pipeline that generates curated question-answer pairs using multiple GPT-4 Omni-based agents with human judgments. Our evaluation protocols measure image hallucination by testing if images from existing text-to-image models can correctly answer these questions. The I-HallA v1.0 dataset comprises 1.2K diverse image-text pairs across 9 categories with varying levels of difficulty and 1,000 questions covering 9 compositions. We evaluate 5 different text-to-image models using I-HallA and demonstrate that these state-of-the-art models often fail to accurately convey factual information. Additionally, we establish the validity of our evaluation method through human evaluation, yielding a Spearman's correlation of 0.95. We believe our benchmark dataset and metric can serve as a foundation for developing factually accurate text-to-image generation models.

Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering

The vision-based geo-localization technology for UAV, serving as a secondary source of GPS information in addition to the global navigation satellite systems (GNSS), can still operate independently when communication with the external environment is cut off.
Recent deep learning based methods attribute this as the task of image matching and retrieval.
By retrieving drone-view images in satellite image database with GPS information tagged, approximate localization information can be obtained.
However, due to high costs and privacy concerns, it is usually difficult to obtain large quantities of drone-view images from a continuous area.
Existing drone-view datasets are mostly composed of small-scale aerial photography with a strong assumption that there exists a perfect one-to-one aligned reference image for any query, leaving a significant gap from the practical localization scenario.
In this work, we construct a large-range continues area UAV geo-localization dataset named GTA-UAV, featuring multiple flight altitudes, attitudes, scenes, and targets using modern computer games.
Based on this dataset, we introduce a more practical UAV geo-localization task including partial matches of cross-view paired data, and expand the image-level retrieval to the actual localization in terms of distance (meters).
For the construction of drone-view and satellite-view pairs, we adopt a weight-based contrastive learning approach, which allows for effective learning while avoiding additional post-processing matching steps.
Experiments demonstrate the effectiveness of our data and training method for UAV geo-localization, as well as the generalization capabilities to real-world scenarios.

Game4Loc: A UAV Geo-Localization Benchmark from Game Data

Wearable Human Activity Recognition (WHAR) is a prominent research area within ubiquitous computing. Multi-sensor synchronous measurement is proven more effective for WHAR than using a single sensor. However, existing WHAR methods use shared convolutional kernels for indiscriminate temporal feature extraction across each sensor variable, which fails to effectively capture spatio-temperal relationships of intra-sensor and inter-sensor variables. We propose the **DecomposeWHAR** model consisting of a Modality-Aware Signal Decomposition phase and a Hierarchical Interaction Fusion phase to better model the relationships between variables. The Decomposition phase creates high-dimensional representations of each intra-sensor variable through Modality-Specific Embedding, followed by Depth-Wise Convolution to capture local temporal features while preserving their unique characteristics. In the fusion phase, the intra-sensor relationships are captured by Point-Wise Convolution at the channel level and variable level. Then we integrate features across the entire temporal level to capture long-range dependencies using the Global Temporal Aggregation module with Mamba. Finally, the cross-sensor interaction is implemented by the self-attention mechanism to capture the inter-sensor spatial correlations. Our model demonstrates superior performance on three widely used WHAR datasets, significantly outperforming State-of-the-Art (SOTA) models while maintaining acceptable computational efficiency.

Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity Recognition

The strategic behavior of users is significantly influenced by their hidden information such as private valuations, risk preferences, and price sensitivities. Contextual behavioral model learning refers to learning the dependence of users' hidden information on their observable context information. While many existing studies use offline data to learn contextual behavioral models, we study how to design sequential experiments to collect the most informative user behavioral data for learning. We propose a basic inference-then-design method. In each experimental period, it infers a probabilistic contextual behavioral model using historical experimental data, and then designs the new experiment to maximize the gain of information about the probabilistic model. We further improve the basic method in two aspects. First, we improve the inference step by specifying a more informative prior for learning the probabilistic contextual behavioral model. Second, we integrate the inference and design steps instead of conducting them separately. Our rigorous theoretic analysis reveals that the optimization objective of the inference step can be modified to account for the downstream experimental design step. Numerical experiments show that our methods lead to more effective experiments, i.e., the collected experimental data can help in learning a more accurate behavioral model.

Downloads

Next from AAAI 2025

Individually Stable Dynamics in Coalition Formation over Graphs

Similar lecture

Improved Algorithms for Learning Equilibria in Simulation-Based Games

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES