Theory and Practice of Time-Space Trade-Offs in Memory Limited Search

Having to cope with memory limitations is an ubiquitous issue in heuristic search. We present theoretical and practical results on new variants for exploring state-space with respect to memory limitations. We establish O(log n) minimum-space algorithms that omit both the open and the closed list to determine the shortest path between every two nodes and study the gap in between full memorization in a hash table and the information-theoretic lower bound. The proposed structure of suffix-lists elaborates on a concise binary representation of states by applying bit-state hashing techniques. Significantly more states can be stored while searching and inserting n items into suffix lists is still available in O(n log n) time. Bit-state hashing leads to the new paradigm of partial iterative-deepening heuristic search, in which full exploration is sacrificed for a better detection of duplicates in large search depth. We give first promising results in the application area of communication protocols.