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

Fuente: arXiv

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Bibliographic Details
Main Authors:	Mu, Chunjiang, Guo, Hao, Chen, Yang, Shen, Chen, Hu, Shuyue, Wang, Zhen
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Computer Science and Game Theory Human-Computer Interaction Machine Learning Multiagent Systems
Online Access:	Acceder al recurso
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_version_	1866929442098511872
author	Mu, Chunjiang Guo, Hao Chen, Yang Shen, Chen Hu, Shuyue Wang, Zhen
author_facet	Mu, Chunjiang Guo, Hao Chen, Yang Shen, Chen Hu, Shuyue Wang, Zhen
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2402_17270
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmas Mu, Chunjiang Guo, Hao Chen, Yang Shen, Chen Hu, Shuyue Wang, Zhen Artificial Intelligence Computer Science and Game Theory Human-Computer Interaction Machine Learning Multiagent Systems 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.
title	Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmas
topic	Artificial Intelligence Computer Science and Game Theory Human-Computer Interaction Machine Learning Multiagent Systems
url	https://arxiv.org/abs/2402.17270