Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmas

• URL: http://arxiv.org/abs/2402.17270v2
• Date: Tue, 30 Jul 2024 12:21:17 GMT
• Title: Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmas
• Authors: Chunjiang Mu, Hao Guo, Yang Chen, Chen Shen, Shuyue Hu, Zhen Wang,
• Abstract summary: The study of cooperation within social dilemmas has long been a fundamental topic across various disciplines. Recent advancements in Artificial Intelligence have significantly reshaped this field. This survey examines three key areas at the intersection of AI and cooperation in social dilemmas.
• Score: 15.785674974107204
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The study of cooperation within social dilemmas has long been a fundamental topic across various disciplines, including computer science and social science. Recent advancements in Artificial Intelligence (AI) have significantly reshaped this field, offering fresh insights into understanding and enhancing cooperation. This survey examines three key areas at the intersection of AI and cooperation in social dilemmas. First, focusing on multi-agent cooperation, we review the intrinsic and external motivations that support cooperation among rational agents, and the methods employed to develop effective strategies against diverse opponents. Second, looking into human-agent cooperation, we discuss the current AI algorithms for cooperating with humans and the human biases towards AI agents. Third, we review the emergent field of leveraging AI agents to enhance cooperation among humans. We conclude by discussing future research avenues, such as using large language models, establishing unified theoretical frameworks, revisiting existing theories of human cooperation, and exploring multiple real-world applications.

Related papers

• How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond [73.5546464126465]
  We present a thorough review of human-model cooperation, exploring its principles, formalizations, and open challenges. We introduce a new taxonomy that provides a unified perspective to summarize existing approaches. Also, we discuss potential frontier areas and their corresponding challenges.
  arXiv  Detail & Related papers  (2025-01-10T05:15:14Z)
• Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration [51.452664740963066]
  Collaborative Gym is a framework enabling asynchronous, tripartite interaction among agents, humans, and task environments. We instantiate Co-Gym with three representative tasks in both simulated and real-world conditions. Our findings reveal that collaborative agents consistently outperform their fully autonomous counterparts in task performance.
  arXiv  Detail & Related papers  (2024-12-20T09:21:15Z)
• Incentivized Symbiosis: A Paradigm for Human-Agent Coevolution [0.0]
  Evolutionary game theory offers a lens to understand the structures and incentives that enable cooperation to be a successful strategy. The convergence of human-agent teaming, contract theory, and decentralized frameworks like Web3 offers a foundation for fostering cooperation. We conceptualize Incentivized Symbiosis as a social contract between humans and AI, inspired by Web3 principles and encoded in blockchain technology.
  arXiv  Detail & Related papers  (2024-12-08T20:23:48Z)
• Multi-agent cooperation through learning-aware policy gradients [53.63948041506278]
  Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. We present the first unbiased, higher-derivative-free policy gradient algorithm for learning-aware reinforcement learning. We derive from the iterated prisoner's dilemma a novel explanation for how and when cooperation arises among self-interested learning-aware agents.
  arXiv  Detail & Related papers  (2024-10-24T10:48:42Z)
• Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task [56.92961847155029]
  Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others. Mutual Theory of Mind (MToM) arises when AI agents with ToM capability collaborate with humans. We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent.
  arXiv  Detail & Related papers  (2024-09-13T13:19:48Z)
• Problem Solving Through Human-AI Preference-Based Cooperation [74.39233146428492]
  We propose HAI-Co2, a novel human-AI co-construction framework. We formalize HAI-Co2 and discuss the difficult open research problems that it faces. We present a case study of HAI-Co2 and demonstrate its efficacy compared to monolithic generative AI models.
  arXiv  Detail & Related papers  (2024-08-14T11:06:57Z)
• CREW: Facilitating Human-AI Teaming Research [3.7324091969140776]
  We introduce CREW, a platform to facilitate Human-AI teaming research in real-time decision-making scenarios. It includes pre-built tasks for cognitive studies and Human-AI teaming with expandable potentials from our modular design. CREW benchmarks real-time human-guided reinforcement learning agents using state-of-the-art algorithms and well-tuned baselines.
  arXiv  Detail & Related papers  (2024-07-31T21:43:55Z)
• 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 Review of Cooperation in Multi-agent Learning [5.334450724000142]
  Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous disciplines. This paper provides an overview of the fundamental concepts, problem settings and algorithms of multi-agent learning.
  arXiv  Detail & Related papers  (2023-12-08T16:42:15Z)
• Adversarial Attacks in Cooperative AI [0.0]
  Single-agent reinforcement learning algorithms in a multi-agent environment are inadequate for fostering cooperation. Recent work in adversarial machine learning shows that models can be easily deceived into making incorrect decisions. Cooperative AI might introduce new weaknesses not investigated in previous machine learning research.
  arXiv  Detail & Related papers  (2021-11-29T07:34:12Z)
• Open Problems in Cooperative AI [21.303564222227727]
  Research aims to study the many aspects of the problems of cooperation and to innovate in AI to contribute to solving these problems. This research integrates ongoing work on multi-agent systems, game theory and social choice, human-machine interaction and alignment, natural-language processing, and the construction of social tools and platforms.
  arXiv  Detail & Related papers  (2020-12-15T21:39:50Z)

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.