Social Network-Oriented Learning Agent for Improving Group Intelligence Coordination

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.