GenAI4ELab/papercli-papers huggingface dataset:
Combining Deep Reinforcement Learning and Search with Generative Models for Game-Theoretic Opponent Modeling
Authors: Zun Li; Marc Lanctot; Kevin R. McKee; Luke Marris; Ian Gemp; Daniel Hennes; Paul Muller; Kate Larson; Yoram Bachrach; Michael P. Wellman
Venue: IJCAI 2025 (main)

Abstract: Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents' strategies, and exploiting this opponent model by playing a best response. However, existing approaches typically require domain-specific heurstics to come up with such a model, and algorithms for approximating best responses are hard to scale in large, imperfect information domains. In this work, we introduce a scalable and generic multiagent training regime for opponent modeling using deep game-theoretic reinforcement learning. We first propose Generative Best Respoonse (GenBR), a best response algorithm based on Monte-Carlo Tree Search (MCTS) with a learned deep generative model that samples world states during planning. This new method scales to large imperfect information domains and can be plug and play in a variety of multiagent algorithms. We use this new method under the framework of Policy Space Response Oracles (PSRO), to automate the generation of an offline opponent model via iterative game-theoretic reasoning and population-based training. We propose using solution concepts based on bargaining theory to build up an opponent mixture, which we find identifying profiles that are near the Pareto frontier. Then GenBR keeps updating an online opponent model and reacts against it during gameplay. We conduct behavioral studies where human participants negotiate with our agents in Deal-or-No-Deal, a class of bilateral bargaining games. Search with generative modeling finds stronger policies during both training time and test time, enables online Bayesian co-player prediction, and can produce agents that achieve comparable social welfare and Nash bargaining score negotiating with humans as humans trading among themselves.
Asynchronous Credit Assignment for Multi-Agent Reinforcement Learning
Authors: Yongheng Liang; Hejun Wu; Haitao Wang; Hao Cai
Venue: IJCAI 2025 (main)

Abstract: Credit assignment is a critical problem in multi-agent reinforcement learning (MARL), aiming to identify agents' marginal contributions for optimizing cooperative policies. Current credit assignment methods typically assume synchronous decision-making among agents. However, many real-world scenarios require agents to act asynchronously without waiting for others. This asynchrony introduces conditional dependencies between actions, which pose great challenges to current methods. To address this issue, we propose an asynchronous credit assignment framework, incorporating a Virtual Synchrony Proxy (VSP) mechanism and a Multiplicative Value Decomposition (MVD) algorithm. VSP enables physically asynchronous actions to be virtually synchronized during credit assignment. We theoretically prove that VSP preserves both task equilibrium and algorithm convergence. Furthermore, MVD leverages multiplicative interactions to effectively model dependencies among asynchronous actions, offering theoretical advantages in handling asynchronous tasks. Extensive experiments show that our framework consistently outperforms state-of-the-art MARL methods on challenging tasks while providing improved interpretability for asynchronous cooperation.
Online Resource Sharing: Better Robust Guarantees via Randomized Strategies
Authors: David X. Lin; Daniel Hall; Giannis Fikioris; Siddhartha Banerjee; Éva Tardos
Venue: IJCAI 2025 (main)

Abstract: We study the problem of fair online resource allocation via non-monetary mechanisms, where multiple agents repeatedly share a resource without monetary transfers. Previous work has shown that every agent can guarantee 1/2 of their ideal utility (the highest achievable utility given their fair share of resources) robustly, i.e., under arbitrary behavior by the other agents. While this 1/2-robustness guarantee has now been established under very different mechanisms, including pseudo-markets and dynamic max-min allocation, improving on it has appeared difficult. In this work, we obtain the first significant improvement on the robustness of online resource sharing. In more detail, we consider the widely-studied repeated first-price auction with artificial currencies. Our main contribution is to show that a simple randomized bidding strategy can guarantee each agent a 2 - √2 ≈ 0.59 fraction of her ideal utility, irrespective of others' bids. Specifically, our strategy requires each agent with fair share α to use a uniformly distributed bid whenever her value is in the top α-quantile of her value distribution. Our work almost closes the gap to the known 1 - 1/e ≈ 0.63 hardness for robust resource sharing; we also show that any static (i.e., budget independent) bidding policy cannot guarantee more than a 0.6-fraction of the ideal utility, showing our technique is almost tight.
Odyssey : Empowering Minecraft Agents with Open-World Skills
Authors: Shunyu Liu; Yaoru Li; Kongcheng Zhang; Zhenyu Cui; Wenkai Fang; Yuxuan Zheng; Tongya Zheng; Mingli Song
Venue: IJCAI 2025 (main)

Abstract: Recent studies have delved into constructing generalist agents for open-world environments like Minecraft. Despite the encouraging results, existing efforts mainly focus on solving basic programmatic tasks, e.g., material collection and tool-crafting following the Minecraft tech-tree, treating the ObtainDiamond task as the ultimate goal. This limitation stems from the narrowly defined set of actions available to agents, requiring them to learn effective long-horizon strategies from scratch. Consequently, discovering diverse gameplay opportunities in the open world becomes challenging. In this work, we introduce Odyssey, a new framework that empowers Large Language Model (LLM)-based agents with open-world skills to explore the vast Minecraft world. Odyssey comprises three key parts: (1) An interactive agent with an open-world skill library that consists of 40 primitive skills and 183 compositional skills. (2) A fine-tuned LLaMA-3 model trained on a large question-answering dataset with 390k+ instruction entries derived from the Minecraft Wiki. (3) A new agent capability benchmark includes the long-term planning task, the dynamic-immediate planning task, and the autonomous exploration task. Extensive experiments demonstrate that the proposed Odyssey framework can effectively evaluate different capabilities of LLM-based agents. All datasets, model weights, and code are publicly available to motivate future research on more advanced autonomous agent solutions.
Deep Learning-Based Pedestrian Simulation with Limited Real-World Training Data: An Evaluation Framework
Authors: Vahid Mahzoon; Abigail Liu; Slobodan Vucetic
Venue: IJCAI 2025 (main)

Abstract: Simulating pedestrian movement is important for applications such as disaster management, robotics, and game design. While deep learning models have been extensively used on related problems, their use as pedestrian simulators remains relatively unexplored. This paper aims to encourage more research in this direction in two ways. First, it proposes an evaluation framework that is applicable to both traditional and deep learning based simulators. Second, it proposes and evaluates several ideas related to input representation, choice of neural architecture, exploiting knowledge-based simulators in data poor regimes, and repurposing trajectory prediction models. Our extensive experiments provide several useful insights for future research in pedestrian simulation. The code is available at https://github.com/vmahzoon76/DL-Crowd-Sim.
Constructive Conflict-Driven Multi-Agent Reinforcement Learning for Strategic Diversity
Authors: Yuxiang Mai; Qiyue Yin; Wancheng Ni; Pei Xu; Kaiqi Huang
Venue: IJCAI 2025 (main)

Abstract: In recent years, diversity has emerged as a useful mechanism to enhance the efficiency of multi-agent reinforcement learning (MARL). However, existing methods predominantly focus on designing policies based on individual agent characteristics, often neglecting the interplay and mutual influence among agents during policy formation. To address this gap, we propose Competitive Diversity through Constructive Conflict (CoDiCon), a novel approach that incorporates competitive incentives into cooperative scenarios to encourage policy exchange and foster strategic diversity among agents. Drawing inspiration from sociological research, which highlights the benefits of moderate competition and constructive conflict in group decision-making, we design an intrinsic reward mechanism using ranking features to introduce competitive motivations. A centralized intrinsic reward module generates and distributes varying reward values to agents, ensuring an effective balance between competition and cooperation. By optimizing the parameterized centralized reward module to maximize environmental rewards, we reformulate the constrained bilevel optimization problem to align with the original task objectives. We evaluate our algorithm against state-of-the-art methods in the SMAC and GRF environments. Experimental results demonstrate that CoDiCon achieves superior performance, with competitive intrinsic rewards effectively promoting diverse and adaptive strategies among cooperative agents.
Beyond Winning Strategies: Admissible and Admissible Winning Strategies for Quantitative Reachability Games
Authors: Karan Muvvala; Qi Heng Ho; Morteza Lahijanian
Venue: IJCAI 2025 (main)

Abstract: Classical reactive synthesis approaches aim to synthesize a reactive system that always satisfies a given specification. These approaches often reduce to playing a two-player zero-sum game where the goal is to synthesize a winning strategy. However, in many pragmatic domains, such as robotics, a winning strategy does not always exist, yet it is desirable for the system to make an effort to satisfy its requirements instead of "giving up." To this end, this paper investigates the notion of admissible strategies, which formalize "doing-your-best", in quantitative reachability games. We show that, unlike the qualitative case, memoryless strategies are not sufficient to capture all admissible strategies, making synthesis a challenging task. In addition, we prove that admissible strategies always exist but may produce undesirable optimistic behaviors. To mitigate this, we propose admissible winning strategies, which enforce the best possible outcome while being admissible. We show that both strategies always exist but are not memoryless. We provide necessary and sufficient conditions for the existence of both strategies and propose synthesis algorithms. Finally, we illustrate the strategies on gridworld and robot manipulator domains.
Navigating Social Dilemmas with LLM-based Agents via Consideration of Future Consequences
Authors: Dung Nguyen; Hung Le; Kien Do; Sunil Gupta; Svetha Venkatesh; Truyen Tran
Venue: IJCAI 2025 (main)

Abstract: Artificial agents with the aid of large language models (LLMs) are effective in various real-world scenarios but struggle to cooperate in social dilemmas. When making decisions under the strain of selecting between long-term consequences and short-term benefits in commonly shared resources, LLM-based agents often exploit the environment, leading to early depletion. Inspired by the concept of consideration of future consequences (CFC), which is well-known in social psychology, we propose a framework to enable the ability to consider future consequences for LLM-based agents, which results in a new kind of agent that we term the CFC-Agent. We enable the CFC-Agent to act toward different levels of consideration for future consequences. Our first set of experiments, where LLM is directly asked to make decisions, shows that agents considering future consequences exhibit sustainable behaviour and achieve high common rewards for the population. Extensive experiments in complex environments showed that the CFC-Agent can manage a sequence of calls to LLM for reasoning and engaging in communication to cooperate with others to resolve the common dilemma better. Finally, our analysis showed that considering future consequences not only affects the final decision but also improves the conversations between LLM-based agents toward a better resolution of social dilemmas.
Multi Objective Quantile Based Reinforcement Learning for Modern Urban Planning
Authors: Lukasz Pelcner; Leandro Soriano Marcolino; Matheus Aparecido do Carmo Alves; Paula A. Harrison; Peter M. Atkinson
Venue: IJCAI 2025 (main)

Abstract: We present a novel Multi-Agent Reinforcement Learning approach to understand and improve policy development by land-shaping agents, such as governments and institutional bodies. We derive the underlying policy decisions by analyzing the land and developing an intelligent system that proposes optimal land conversion strategies. The aim is an efficient method for allocating residential spaces while considering the dynamic population influx in different regions, jurisdictional constraints, and the intrinsic characteristics of the land. Our main goal is to be sustainable, preserving desirable land types such as forests and fluvial lands while optimizing land organization. We introduce an attractiveness metric that quantifies the proximity to different land types and other factors to optimize land usage. It distinguishes two types of agents: ``top-down'' agents, which are policymakers and shareholders, and ``bottom-up'' agents representing individuals or groups with specific housing preferences. Our main objective is to create a synergistic environment where the top-down policy meets the bottom-up preferences to devise a comprehensive land use and conversion strategy. This paper, thus, serves as a pivotal reference point for future urban planning and policy-making processes, contributing to a sustainable and efficient landscape design model.
Multi-Agent Corridor Generating Algorithm
Authors: Arseni Pertzovskiy; Roni Stern; Roie Zivan; Ariel Felner
Venue: IJCAI 2025 (main)

Abstract: In this paper, we propose the Multi-Agent Corridor Generating Algorithm (MACGA) for solving the Multi-agent Pathfinding (MAPF) problem, where a group of agents need to find non-colliding paths to their target locations. Existing approaches struggle to solve dense MAPF instances. In MACGA, the agents build corridors, which are sequences of connected vertices, from current locations towards agents' goals, and evacuate other agents out of the corridors to avoid collisions and deadlocks. We also present the MACGA+PIBT algorithm, which integrates the well-known rule-based PIBT algorithm into MACGA to improve runtime and solution quality. The proposed algorithms run in polynomial time and have a reachability property, i.e., every agent is guaranteed to reach its goal location at some point. We demonstrate experimentally that MACGA and MACGA+PIBT outperform baseline algorithms in terms of success rate, runtime, and makespan across diverse MAPF benchmark grids.
Simulating Misinformation Diffusion on Social Media Through CoNVaI: A Textual- and Agent-Based Diffusion Model
Authors: Raquel Rodríguez-García; Roberto Centeno; Álvaro Rodrigo
Venue: IJCAI 2025 (main)

Abstract: Misinformation has experienced increased online diffusion, leveraging strategies, such as emotional manipulation, to influence users' opinions. Efforts are underway to develop tools to mitigate its effects, such as misinformation propagation models used to simulate the diffusion of information. There are different approaches within these models, although, they show a significant limitation by disregarding the content of the information shared, crucial to the diffusion. We consider it the central aspect of modeling information dissemination. To this end, we focus on Agent-Based Modeling due to its suitability to simulate the complex interactions and heterogeneous behaviors observed on social media. We base our approach on a state-of-the-art Agent-Based Model that we modify and extend to account for the texts of the messages shared, focusing on two aspects that influence agents' decisions: i) the novelty of the content and; ii) its diffusion and behavior over time. To determine whether this content proves informative, we conduct an empirical evaluation using social media data from Twitter. Based on our experimental results, we observe that our textual-based approach reflects information diffusion more realistically than the state of the art, reducing the error regarding real diffusion.
Synthesis of Communication Policies for Multi-Agent Systems Robust to Communication Restrictions
Authors: Saleh Soudijani; Rayna Dimitrova
Venue: IJCAI 2025 (main)

Abstract: We study stochastic multi-agent systems in which agents must cooperate to maximize the probability of achieving a common reach-avoid objective. In many applications, during the execution of the system, the communication between the agents can be constrained by restrictions on the bandwidth currently available for exchanging local-state information between the agents. In this paper, we propose a method for computing joint action and communication policies for the group of agents that aim to satisfy the communication restrictions as much as possible while achieving the optimal reach-avoid probability when communication is unconstrained. Our method synthesizes a pair of action and communication policies robust to restrictions on the number of agents allowed to communicate. To this end, we introduce a novel cost function that measures the amount of information exchanged beyond what the communication policy allows. We evaluate our approach experimentally on a range of benchmarks and demonstrate that it is capable of computing pairs of action and communication policies that satisfy the communication restrictions, if such exist.
Streaming Multi-agent Pathfinding
Authors: Mingkai Tang; Lu Gan; Kaichen Zhang
Venue: IJCAI 2025 (main)

Abstract: The task of the multi-agent pathfinding (MAPF) problem is to navigate a team of agents from their start point to the goal points. However, this setup is unsuitable in the assembly line scenario, which is periodic with a long working hour. To address this issue, the study formalizes the streaming MAPF (S-MAPF) problem, which assumes that the agents in the same agent stream have a periodic start time and share the same action sequence. The proposed solution, Agent Stream Conflict-Based Search (ASCBS), is designed to tackle this problem by incorporating a cyclic vertex/edge constraint to handle conflicts. Additionally, this work explores the potential usage of the disjoint splitting strategy within ASCBS. Experimental results indicate that ASCBS surpasses traditional MAPF solvers in terms of runtime for scenarios with prolonged working hours.
Equitable Mechanism Design for Facility Location
Authors: Toby  Walsh
Venue: IJCAI 2025 (main)

Abstract: We consider strategy proof mechanisms for facility location which maximize equitability between agents. As is common in the literature, we measure equitability with the Gini index. We first prove a simple but fundamental impossibility result that no strategy proof mechanism can bound the approximation ratio of the optimal Gini index of utilities for one or more facilities. We propose instead computing approximation ratios of the complemented Gini index of utilities, and consider how well both deterministic and randomized mechanisms approximate this. In addition, as Nash welfare is often put forwards as an equitable compromise between egalitarain and utilitarian outcomes, we consider how well mechanisms approximate the Nash welfare.
Quantifying the Self-Interest Level of Markov Social Dilemmas
Authors: Richard Willis; Yali Du; Joel Z. Leibo; Michael Luck
Venue: IJCAI 2025 (main)

Abstract: This paper introduces a novel method for estimating the self-interest level of Markov social dilemmas. We extend the concept of self-interest level from normal-form games to Markov games, providing a quantitative measure of the minimum reward exchange required to align individual and collective interests. We demonstrate our method on three environments from the Melting Pot suite, representing either common-pool resources or public goods. Our results illustrate how reward exchange can enable agents to transition from selfish to collective equilibria in a Markov social dilemma. This work contributes to multi-agent reinforcement learning by providing a practical tool for analysing complex, multistep social dilemmas. Our findings offer insights into how reward structures can promote or hinder cooperation, with potential applications in areas such as mechanism design.
CoderAgent: Simulating Student Behavior for Personalized Programming Learning with Large Language Models
Authors: Yi Zhan; Qi Liu; Weibo Gao; Zheng Zhang; Tianfu Wang; Shuanghong Shen; Junyu Lu; Zhenya Huang
Venue: IJCAI 2025 (main)

Abstract: Personalized programming tutoring, such as exercise recommendation, can enhance learners' efficiency, motivation, and outcomes, which is increasingly important in modern digital education. However, the lack of sufficient and high-quality programming data, combined with the mismatch between offline evaluation and real-world learning, hinders the practical deployment of such systems. To address this challenge, many approaches attempt to simulate learner practice data, yet they often overlook the fine-grained, iterative nature of programming learning, resulting in a lack of interpretability and granularity. To fill this gap, we propose a LLM-based agent, CoderAgent, to simulate students' programming processes in a fine-grained manner without relying on real data. Specifically, we equip each human learner with an intelligent agent, the core of which lies in capturing the cognitive states of the human programming practice process. Inspired by ACT-R, a cognitive architecture framework, we design the structure of CoderAgent to align with human cognitive architecture by focusing on the mastery of programming knowledge and the application of coding ability. Recognizing the inherent patterns in multi-layered cognitive reasoning, we introduce the Programming Tree of Thought (PTOT), which breaks down the process into four steps: why, how, where, and what. This approach enables a detailed analysis of iterative problem-solving strategies. Finally, experimental evaluations on real-world datasets demonstrate that CoderAgent provides interpretable insights into learning trajectories and achieves accurate simulations, paving the way for personalized programming education.
Formal Synthesis of Safe Kolmogorov-Arnold Network Controllers with Barrier Certificates
Authors: Xiongqi Zhang; Ning Lv; Wang Lin; Zuohua Ding
Venue: IJCAI 2025 (main)

Abstract: Control barrier certificate generation is an efficient and powerful technique for the safe control of cyber-physical systems. Feed-forward neural networks (FNNs) are commonly used to synthesize control barrier certificates and safe controllers, but they struggle to effectively address the challenges posed by high-dimensional complex systems. In this paper, we propose a novel method for generating control barrier certificates and controllers using Kolmogorov-Arnold Networks (KANs). Specifically, it utilizes KANs to replace FNNs as the template of control barrier certificates and contrllers. Since KAN has learnable activation functions, it can efficiently improve the representation power. Then, it leverages the pruning and symbolization properties of KANs, which significantly simplify the network structure, allowing for more efficient formal verification of the simplified candidate KAN control barrier certificates and controllers using Satisfiability Modulo Theories. We implement the tool KAN4CBC, and evaluate its performance over a set of benchmarks. The experimental results demonstrate that our method addresses the issues of system dimension expansion and improved solution efficiency.