Spread of Cooperation in Complex Agent Networks Based on Expectation of Cooperation

Shibusawa, Ryosuke; Otsuka, Tomoaki; Sugawara, Toshiharu

doi:10.1007/978-3-319-44832-9_5

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Peleteiro et al [28] presented a new mechanism based on three main pillars (indirect reciprocity, coalitions and rewiring) to improve cooperation among self-interested agents placed in a reputation-based game. Shibusawa et al [29] proposed a behavioral strategy called the expectation of cooperation, which is simple and easy to implement but, nevertheless, can spread over and maintain cooperation in agent networks under certain conditions. However, these studies are not applicable to solving the social dilemma of coordination in some agent networks, or they are not applicable to actual social systems.…”

Section: Introductionmentioning

confidence: 99%

Social Network-Oriented Learning Agent for Improving Group Intelligence Coordination

Bai

Xiao

2019

IEEE Access

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Group coordination is embedded in social networks, which aims to reach a consensus or solve conflicts among social individuals. Recently, improving performance has become a challenging task and drawn considerable interest in this field. To eliminate barriers of group coordination, we abstract the problem into a networked color coordination game and introduce learning agents encoded with Q-learning to collect local information and learn local individual behaviors. We first show that learning agents can effectively improve the group coordination performance. By properly selecting parameters, we find that learning agents acting with low greedy parameter levels and placed in central locations can drastically accelerate group coordination. Greedy parameters and positions are vital to the problem. Finally, we indicate that learning agents have a direct effect on neighbor individuals and an indirect effect on non-neighbor individuals to act on the coordination network. Moreover, we propose a conflict relationship index which is the average rounds required for solving conflicts and indicate learning agents solve conflicts that cannot be solved by an individual. Hence, learning agents create further benefits to group coordination in these complex social networks. This paper provides a detailed analysis of the learning agents in a networked color coordination game and shows that artificial intelligence provides a solution to the group coordination problem. INDEX TERMS Group coordination, learning agents, reinforcement learning.

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Section: Introductionmentioning

confidence: 99%