![]() ![]() ![]() This multi-agent interactions have attracted attention in several scientific and engineering fields, such as biology, human behavioral science, including modeling, and social psychology, and robotics including simulated agents. This type of cooperation has been referred to as collective intelligence, team synergy, or teamwork. Groups of social organisms, such as fishes, birds, and humans including people performing sports, cooperate to achieve common goals that could not be achieved by an individual. The proposed framework, which sheds light on inconspicuous players to play important roles, could have a potential to detect well-defined and labeled cooperative behaviors. We also propose a method to classify more detailed types of cooperative plays in various situations. Using player’s moving distance, geometric information, and distances among players, the proposed method accurately discriminated not only the cooperative plays in a primary area, i.e., near the ball, but also those distant from a primary area. Here, we propose an automatic recognition system for strategic cooperative plays, which are the minimal, basic, and diverse plays in a ball game. In other words, it is difficult to find similar and different structures of the motions with the same and different labels, respectively. However, these actions which are manually categorized with the same label based on its function have low spatiotemporal similarity. In some cases, such as team sports, many cooperative behaviors can be visually categorized and labeled manually by experts. Understanding multi-agent cooperative behavior is challenging in various scientific and engineering domains. ![]()
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