Applying Means-ends Analysis to Spatial Planning

Reasoning with Diagrammatic Representations

Volume

Reasoning with Diagrammatic Representations

Contents

Currently known methods for robot planning fall far behind human capabUities: they require approximations of shapes, and they cannot generate plans which involve moving obstacles to clear a path for the moving object. In this paper, we explore the hypothesis that means-ends analysis baaed on a world model allows more human-like solutions. Our method is baaed on a novel way of representing planning constraints which makes it possible to incrementally generate the symbolic representations for means-ends planning using only imagery operations.