Using qualitative reasoning in proving achievability

Abstract

A method is presented by which planning in domains which are neither perfectly characterizable nor completely unpredictable might be achieved. The primary goal of this research is the development of learning strategies which will enable a planner to learn how to construct plans useful and usable in real-world domains. In line with this goal, the author has developed an integrated planning approach in which a classical planner is augmented with the ability to defer achievable goals, which are addressed during execution. The completable reactive plans constructed in this approach remain provably correct due to the achievability constraint on deferred goals, while allowing a planner to use runtime information to facilitate its planning.<>