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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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A relatively simple and local interaction between individuals produces coordinated and ordered collective behaviors that are widespread at all levels of biological groups. Group chase and escape is an important aspect in the field of collective behavior, particularly in regard to predation events in species interactions. Compared with other aspects of collective behavior, less research has been performed on this aspect, and the existing models are constructed only from the phenomenological perspective. We present an individual-based model named Visual Perception-Decision-Propulsion to explore the group chase and escape of biological groups and define several evaluation indicators to assess different aspects of this problem. Within this model, 2 types of self-propulsion individuals, i.e., predators and prey, are considered, and we consider the alignment and repulsion term between homogeneous individuals. Chase and escape are described as the escape (or chase) term between heterogeneous individuals. Based on the model, we identify and distinguish between 2 capture patterns, i.e., cooperative capture and separative capture. Then, we control the internal parameters to analyze the condition of these 2 patterns for production, and the external empirical parameters are adjusted to explore their effect on these 2 patterns. Hence, this paper provides a novel model for group chase and escape based on biological vision to compensate for the shortcomings of classical models and help apply the characteristics of biological groups to human-made swarm systems in the case of confrontation. INDEX TERMS Collective behavior, visual perception, group chase and escape.