R. Korf's Selected Publications If you're interested in any of these papers, please send email to korf@cs.ucla.edu, and I'll be happy to send you copies. * (with Ariel Felner) Recent progress in heuristic search: A case study of the four-peg towers of hanoi problem, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, Jan. 2007, pp. 2334-2329. Abstract: We integrate a number of new and recent advances in heuristic search, and apply them to the four-peg Towers of Hanoi problem. These include frontier search, disk-based search, parallel processing, multiple, compressed, disjoint, and additive pattern database heuristics, and breadth-first heuristic search. New ideas include pattern database heuristics based on multiple goal states, a method to reduce coordination among multiple parallel threads, and a method for reducing the number of heuristic calculations. We perform the first complete breadth-first searches of the 21 and 22-disc four-peg Towers of Hanoi problems, and extend the verification of ``presumed optimal solutions'' to this problem from 24 to 31 discs. * (with Weixiong Zhang, Ignacio Thayer, and Heath Hohwald), Frontier search, Journal of the Association for Computing Machinery, Vol. 52, No. 5, Sept. 2005, pp. 715-748. Abstract: The critical resource that limits the application of best-first search is memory. We present a new class of best-first search algorithms that reduce the space complexity. The key idea is to store only the Open list of generated nodes, but not the Closed list of expanded nodes. The solution path can be recovered by a divide-and-conquer technique, either as a bidirectional or unidirectional search. For many problems, frontier search dramatically reduces the memory required by best-first search. We apply frontier search to breadth-first search of sliding-tile puzzles and the 4-peg Towers of Hanoi problem, Dijkstra's algorithm on a grid with random edge costs, and the A* algorithm on the Fifteen Puzzle, the four-peg Towers of Hanoi Problem, and optimal sequence alignment in computational biology.