Applying means-ends analysis to spatial planning

Existing methods for robot planning fall far behind human capabilities: they require approximations of shapes, and they cannot generate plans which involve moving obstacles to clear a path for the moving object. The authors explore the hypothesis that means-ends analysis based on a world model involving mental imagery allows more human-like solutions. The method is based on a way or representing planning constraints which makes it possible to generate incrementally the symbolic representations for means-ends planning using only imagery operations