[3A1-GS-5] Agents:

[3A1-GS-5-05] Investigating the Process of Recognizing Others in Agents Using Reinforcement Learning

〇Takafumi Sakamoto¹, Yugo Takeuchi¹ (1. Shizuoka University)

Keywords:Human-Agent Interaction, Social Cognition, Computational Simulation

This study explores the dynamics of internal state changes in agents through reinforcement learning to enhance socially adaptive behaviors in public interactions. Recognizing the necessity for communication robots to adjust their actions based on the behaviors and situations of surrounding individuals, we design agents capable of modifying their internal states in response to the internal states of others. By simulating interactions, we investigate the impact of these experiences on the agents' internal state changes. Our approach leverages Q-learning to model the adaptive changes in agents' internal states, focusing on interactions that necessitate consideration of others. In conclusion, our findings suggest that for scenarios requiring estimating others' latent internal states, merely inferring these states from momentary behaviors is inadequate. There is a need for models that incorporate temporal dimensions to make more accurate predictions.