Hideaki Misawa

Decision making based on reinforcement learning and emotion learning for social behavior

In this paper, we propose a decision making method based on reinforcement learning and emotion learning (DRE) for inducing social behaviors of robots. Emotion of animals has an important role in their social interactions. We attempt to incorporate emotion into decision making of robots. To make a social decision making, the DRE combines a decision based on intrinsic fear emotion with a strategic decision obtained by reinforcement learning. Agents with the DRE learn state values by reinforcement learning and learn emotion values by fear emotion learning. In simulation experiments, the effectiveness of the DRE is verified concerning the emergence of social behaviors and the adaptability to an environmental change through an unmoving target search problem.

Multi-space Competitive DGA and its application to localization of multiple signal sources

In this paper, a new optimization method, which is effective for the problem that the optimum solution should be searched in several solution spaces, is proposed. The proposed method is an extension of distributed genetic algorithm (DGA), in which each sub-population searches a solution in different space. Based on the competition between sub-populations, population sizes are adequately changed. The proposed method is applied to signal source localization, in which the number of sources is unknown, and simulation results show the effectiveness of the method.