Advanced Game-Theoretic Frameworks for Multi-Agent AI Challenges: A 2025 Outlook

• URL: http://arxiv.org/abs/2506.17348v1
• Date: Thu, 19 Jun 2025 17:26:03 GMT
• Title: Advanced Game-Theoretic Frameworks for Multi-Agent AI Challenges: A 2025 Outlook
• Authors: Pavel Malinovskiy,
• Abstract summary: We provide a set of mathematical formalisms, simulations, and coding schemes that illustrate how multi-agent AI systems may adapt and negotiate in complex environments.<n>This work aims to equip AI researchers with robust theoretical tools for aligning strategic interaction in uncertain, partially adversarial contexts.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a substantially reworked examination of how advanced game-theoretic paradigms can serve as a foundation for the next-generation challenges in Artificial Intelligence (AI), forecasted to arrive in or around 2025. Our focus extends beyond traditional models by incorporating dynamic coalition formation, language-based utilities, sabotage risks, and partial observability. We provide a set of mathematical formalisms, simulations, and coding schemes that illustrate how multi-agent AI systems may adapt and negotiate in complex environments. Key elements include repeated games, Bayesian updates for adversarial detection, and moral framing within payoff structures. This work aims to equip AI researchers with robust theoretical tools for aligning strategic interaction in uncertain, partially adversarial contexts.

Related papers

• Hypergames: Modeling Misaligned Perceptions and Nested Beliefs for Multi-agent Systems [3.5083201638203154]
  We present a systematic review of agent-compatible applications of hypergame theory.<n>We analyze 44 selected studies from cybersecurity, robotics, social simulation, communications, and general game-theoretic modeling.<n>Our analysis reveals prevailing tendencies, including the prevalence of hierarchical and graph-based models in deceptive reasoning.
  arXiv  Detail & Related papers  (2025-07-25T18:06:41Z)
• Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats [15.764094200832071]
  Traditional cybersecurity methods rely on manual responses and brittles.<n>Game theory provides a rigorous foundation for modeling adversarial behavior.<n>Agentic AI reshapes software design: systems must now be modular, adaptive, and trust-aware.
  arXiv  Detail & Related papers  (2025-07-14T00:49:44Z)
• FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theory [51.96049148869987]
  We present FAIRGAME, a Framework for AI Agents Bias Recognition using Game Theory.<n>We describe its implementation and usage, and we employ it to uncover biased outcomes in popular games among AI agents.<n>Overall, FAIRGAME allows users to reliably and easily simulate their desired games and scenarios.
  arXiv  Detail & Related papers  (2025-04-19T15:29:04Z)
• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users.<n>This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems [132.77459963706437]
  This book provides a comprehensive overview, framing intelligent agents within modular, brain-inspired architectures.<n>It explores self-enhancement and adaptive evolution mechanisms, exploring how agents autonomously refine their capabilities.<n>It also examines the collective intelligence emerging from agent interactions, cooperation, and societal structures.
  arXiv  Detail & Related papers  (2025-03-31T18:00:29Z)
• Mastering the Digital Art of War: Developing Intelligent Combat Simulation Agents for Wargaming Using Hierarchical Reinforcement Learning [0.0]
  dissertation proposes a comprehensive approach, including targeted observation abstractions, multi-model integration, a hybrid AI framework, and an overarching hierarchical reinforcement learning framework. Our localized observation abstraction using piecewise linear spatial decay simplifies the RL problem, enhancing computational efficiency and demonstrating superior efficacy over traditional global observation methods. Our hybrid AI framework synergizes RL with scripted agents, leveraging RL for high-level decisions and scripted agents for lower-level tasks, enhancing adaptability, reliability, and performance.
  arXiv  Detail & Related papers  (2024-08-23T18:50:57Z)
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
  Article explores the convergence of connectionist and symbolic artificial intelligence (AI) Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
  arXiv  Detail & Related papers  (2024-07-11T14:00:53Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• A call for embodied AI [1.7544885995294304]
  We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development.
  arXiv  Detail & Related papers  (2024-02-06T09:11:20Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
  ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
  arXiv  Detail & Related papers  (2023-08-22T10:36:56Z)
• Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective [69.25513235556635]
  Adversarial machine learning (AML) studies the adversarial phenomenon of machine learning, which may make inconsistent or unexpected predictions with humans. Some paradigms have been recently developed to explore this adversarial phenomenon occurring at different stages of a machine learning system. We propose a unified mathematical framework to covering existing attack paradigms.
  arXiv  Detail & Related papers  (2023-02-19T02:12:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.