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 Atari-like 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 best-first width search for satisficing classical planning. Finally, we empirically show the resulting planners to significantly improve the state of the art in satisficing planning.