[2F1-GS-1] Fundamental AI, theory: algorithm

[2F1-GS-1-03] The Role of Causal Induction in Communication Games in which Players Read Each Other

〇Ryota Hayashi¹, Nobuhito Manome², Tatsuji Takahashi¹, Shuji Shinohara^1,2 (1. Tokyo Denki University, 2. Graduate School of Engineering, The University of Tokyo)

Keywords:Bayesian inference, Symmetry bias, Causal inference

Humans communicate by utilizing both external cues, such as speech and facial expression, which constitutes the information, as well as internal cues like emotions and feelings. Since the other person's internal information cannot be immediately observed, it must be guessed using the information that can be seen from the outside in order to promote smooth conversation. Bayesian inference is one technique for facilitating this estimation; it involves developing several hypotheses beforehand and selecting the best suitable one based on observed data. This method is very effective when the target of estimation is stationary, but when dealing with something that changes constantly, like human emotions, another approach is necessary. Therefore, the focus of this study is on an expanded Bayesian inference that takes into account the symmetry bias, a cognitive bias, and introduces a forgetting rate and learning rate. The efficacy of this estimating approach will be examined by simulating a communication game in which two decision-making agents, each equipped with this estimation method, estimate the other's internal generative models.