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

Author:

Hao Guo, Chunjiang Mu, Yang Chen, Chen Shen, Shuyue Hu, Zhen Wang

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Computer Science and Game Theory (cs.GT), Human-Computer Interaction (cs.HC), Machine Learning (cs.LG), Multiagent Systems (cs.MA)

journal:

date:

2024-02-27 00:00:00

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.

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