Group Behavior Learning in Multi-Agent Systems Based on Social Interaction Among Agents

Research on multi-agent systems, in which autonomous agents are able to learn cooperative behavior, has been the subject of rising expectations in recent years. We have aimed at the group behavior generation of the multi-agents who have high levels of autonomous learning ability, like that of human beings, through social interaction between agents to acquire cooperative behavior. The sharing of environment states can improve cooperative ability, and the changing state of the environment in the information shared by agents will improve agents’ cooperative ability. On this basis, we use reward redistribution among agents to reinforce group behavior, and we propose a method of constructing a multi-agent system with an autonomous group creation ability. This is able to strengthen the cooperative behavior of the group as social agents

Self-localization based on Image Features of Omni-directional Image

Omni-vision system using an omni-mirror is popular to acquire environment information around an autonomous mobile robot. In RoboCup soccer middle size robot league in particular, self-localization methods based on white line extraction on the soccer field are popular. We have studied a self-localization method based on image features, for example, SIFT and SURF, so far. Comparative studies with a conventional self-localization method based on white line extraction are conducted. Compared to the self-localization method based on white line extraction, the method based on image feature can be applied to a general environment with a compact database

Deletion of the RUNX1 binding site in the erythroid cell-specific regulatory element of the ABO gene in two individuals with the Am phenotype

学位記番号:医博甲154