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 approach to proving unsolvability, and apply it to RBFAOO and BLDFS, two state-of-theart algorithms. We demonstrate the performance in
domain-independent nondeterministic planning