mlsanityData to 14 Oct 2026

Cognitive Insights and Stable Coalition Matching for Fostering Multi-Agent Cooperation

arXiv 2405.18044 ↗First posted 28 May 2024cs.MA cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Cognitive abilities, such as Theory of Mind (ToM), play a vital role in facilitating cooperation in human social interactions. However, our study reveals that agents with higher ToM abilities may not necessarily exhibit better cooperative behavior compared to those with lower ToM abilities. To address this challenge, we propose a novel matching coalition mechanism that leverages the strengths of agents with different ToM levels by explicitly considering belief alignment and specialized abilities when forming coalitions. Our proposed matching algorithm seeks to find stable coalitions that maximize the potential for cooperative behavior and ensure long-term viability. By incorporating cognitive insights into the design of multi-agent systems, our work demonstrates the potential of leveraging ToM to create more sophisticated and human-like coordination strategies that foster cooperation and improve overall system performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 48 references that are in the map. First posted on arXiv 28 May 2024.

Builds on

25 of 48 references.

References · 25

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  4. 2021Program Synthesis with Large Language Modelscited by 3,637
  5. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,395
  6. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 1,011
  7. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 742
  8. 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 650
  9. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  10. 2023A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesiscited by 353
  11. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agentscited by 333
  12. 2023Exploring Large Language Models for Communication Games: An Empirical Study on Werewolfcited by 241
13 more
  1. 2023Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedbackcited by 207
  2. 2023SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Taskscited by 168
  3. 2023War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Warscited by 145
  4. 2023How FaR Are Large Language Models From Agents with Theory-of-Mind?cited by 76
  5. 2019Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learningcited by 68
  6. 2023LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplaycited by 63
  7. 2021ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mindcited by 58
  8. 2020DebateSum: A large-scale argument mining and summarization datasetcited by 34
  9. 2023Self-Adaptive Large Language Model (LLM)-Based Multiagent Systemscited by 31
  10. 2023Cooperation on the Fly: Exploring Language Agents for Ad Hoc Teamwork in the Avalon Gamecited by 28
  11. 2020Towards Learning Multi-agent Negotiations via Self-Playcited by 17
  12. 2017Understanding State Preferences With Text As Data: Introducing the UN General Debate Corpuscited by 11
  13. 2024Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks Worldcited by 9

Built on by

Not cited yet.