Acquiring a Probabilistic Map with Dialogue-Based Learning

This paper describes an experiment where dialogue-based learning is applied to map acquisition of a mobile O ce-Conversant robot. The system learns the map of environment through simple dialogue with human teachers. A formal probabilistic model is introduced as a representation of map. The importance and the e ectiveness of proper segmentation of spatial-action space and statistical inference using estimated probability distribution on the segmented representation are shown.