An architecture for building "potential field" cognitive maps in mobile robot navigation

This paper describes a fresh interpretation of, and experimental modifications to, an architecture which has arisen from the author\´s previous work (1997). The previous published work concentrates upon the learning structure adopted, which is based on an adaptive heuristic critic. This paper focuses upon the nature of the knowledge representation acquired by the architecture, and in particular on the case of "latent" or "reward-free" learning. The purpose of this investigation is to show that our architecture can perform latent learning, and that this knowledge can be used to improve the performance of subsequent reward-based learning phases. The results of simulation experiments which have been devised to test performance under these circumstances are given.