Red-Black Relaxed Plan Heuristics Reloaded

Abstract

Despite its success, the delete relaxation has significant pitfalls. In an attempt to overcome these pitfalls, recent work has devised so-called red-black relaxed plan heuristics, where red variables take the relaxed semantics (accumulating their values), while black variables take the regular semantics. These heuristics were shown to significantly improve over standard delete relaxation heuristics. However, the experiments also brought to light a major weakness: Being based on repairing fully delete-relaxed plans, the returned estimates depend on arbitrary choices made in such plans. This can lead to huge over-estimation in arbitrary subsets of states. Here we devise a new red-black planning method not based on repairing relaxed plans, getting rid of much of this variance. Our experiments show a significant improvement over previous red-black relaxed plan heuristics, and other related methods.

Partial Delete Relaxation, Unchained: On Intractable Red-Black Planning and Its Applications

“…Recent partial delete relaxation methods take this idea to the extreme: they allow, in principle, to force relaxed plans to behave like real plans in the limit, thus interpolating all the way between delete-relaxed planning and real planning. Two such methods are known, namely explicit conjunctions (Haslum 2012;Keyder, Hoffmann, and Haslum 2012;, which forces relaxed plans to more accurately handle a given set C of conjunctions; and red-black planning (Katz, Hoffmann, and Domshlak 2013b;2013a;Katz and Hoffmann 2013;Domshlak, Hoffmann, and Katz 2015), which delete-relaxes only a subset of the state variables (the "red" ones), keeping the original semantics of the other ("black") variables.…”