Adapting Novelty to Classical Planning as Heuristic Search

The introduction of the concept of state novelty has advanced the state of the art in deterministic online planning in Atarilike problems and in planning with rewards in general, when rewards are defined on states. In classical planning, however, the success of novelty as the dichotomy between novel and non-novel states was somewhat limited. Until very recently, novelty-based methods were not able to successfully compete with state-of-the-art heuristic search based planners. In this work we adapt the concept of novelty to heuristic search planning, defining the novelty of a state with respect to its heuristic estimate. We extend the dichotomy between novel and non-novel states and quantify the novelty degree of state facts. We then show a variety of heuristics based on the concept of novelty and exploit the recently introduced bestfirst width search for satisficing classical planning. Finally, we empirically show the resulting planners to significantly improve the state of the art in satisficing planning.