Depth-First Memory-Limited AND/OR Search and Unsolvability in Cyclic Search Spaces

Abstract

Computing cycle-free solutions in cyclic AND/OR search spaces is an important AI problem. Previous work on optimal depth-first search strongly assumes the use of consistent heuristics, the need to keep all examined states in a transposition table, and the existence of solutions. We give a new theoretical analysis under relaxed assumptions where previous results no longer hold. We then present a generic approachto proving unsolvability, and apply it to RBFAOO and BLDFS, two state-of-the-art algorithms. We demonstrate the performance in domain-independent nondeterministic planning

Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review

“…It prunes branches of the search tree based on these proof and disproof numbers to focus on the most promising lines of play. This search algorithm is used in chemistry in DFPN-E, 88 where authors couple DFPN with a heuristic edge initialization method to address the imbalance between the numbers of OR versus AND moves, later improved to output multiple solutions. 89 The CompRet framework 90 proposes a comprehensive tool to enumerate and rank possible routes to synthesize compounds, using metrics derived from the SCScore to recommend most promising routes.…”

Parallel AND/OR Search for Marginal MAP

“…To discuss whether or not search can return an optimal solution tree cost, we extend to parallel search the notion of memory requirement linear in the search depth and the branching factor along the path, which is introduced in (Korf 1993;Kishimoto, Marinescu, and Botea 2019). An additional structure linear to the number of MAP variables is necessary to return an actual assignment.…”

“…The following lemma which is a modification to Lemma 7 in (Kishimoto, Marinescu, and Botea 2019) holds. The proof of Theorem 3 below requires the lemma.…”