A Ball Game Type Interaction Based on Anthropomorphic Agents Reflecting User’s Tendency of Giving Reward

Abstract

This research aims to develop a human-agent interaction that users can genuinely enjoy. We expect that to enhance positive impressions, the human-agent interactions should reflect the users’ own preferences. Thus, we have developed an agent that satisfies this goal. This agent dynamically learns the reward gained by the user-agent interaction and thereby improves its interaction with the user. In turn, the better interaction enhances the user’s impression of the agent. The effectiveness of our agent was experimentally assessed in a simulated ball game between the agent and a user. In a sensitivity evaluation with real participants, users were favorably impressed by the agent. In the present study, the effectiveness of our proposed technique is tested in more diverse interactions and environments that enable emergent behavior. For this purpose, we selected a ball game environment and proposed a learning model based on the ball game activity. The effectiveness of the human-agent interaction was verified in an experimental sensitivity evaluation. Using a simulator, we constructed human agents that interacted with agents in a ball game environment. To confirm that our proposed system could impress users and create a variety of user-enjoyable interactions, we conducted a sensitivity analysis on participants. The reward grant frequencies assigned by the different participants largely influenced the interaction and sensitivity evaluations by the participants.