Computational Understanding of Belief System in Experiential Learning

Abstract

A learner can understand the complex and undetermined world through experiential learning. This study considers experiential learning as a process of symbol emergence derived from multimodal interactions between a learner and the real world. The purpose of this study is to construct the computational representation of a learner's internal information processing to generate experiences, and to develop a basic model for estimating the generation state of experience. A feature of our model is integrating a belief system (often implemented in robotics) with cognitive science. We analyzed multimodal data of environmental learning as a model case of experiential learning. Our qualitative analysis found that behaviors to update past experiences by behavior results and to generate experiences by belief. This study can be a basis for the next generation of HCI (Human-Computation Interaction) research, including the development of intelligent user interfaces that encourage people to change their behavior.