Learning planning operators in real-world, partially observable environments

Abstract

We are interested in the development of activities in situated, embodied agents such as mobile robots. Cen-tral to our theory of development is means-ends anal-ysis planning, and as such, we must rely on operator models that can express the eects of a robot's action in a dynamic, partially-observable environment. This paper presents a two-step process which employs clus-tering and decision tree induction to perform unsuper-vised learning of operator models from simple interac-tions between an agent and its environment. We report our ndings with an implementation of this system on a Pioneer-1 mobile robot.