Richard E. Korf

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We apply the two-pluyer game assumprio~ls of 1i111ited search horizon and cornn~itnrent to nroves i constant time, to .single-agent heuristic search problems. We present a varicrtion of nrinimcr lookuhead search, and an analog to ulphu-betu pruning rlrot signijicantly improves the efficiency c. the algorithm. Paradoxically. the search horizon reachuble with(More)
The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. I t is known that breadth-first search requires too much space and depth-first search can use too much time and doesn't always find a cheapest path. A depth-first iteratiw-deepening algorithm is shown to be asymptotically optimal along al l three(More)
Korf, R.E., Linear-space best-first search, Artificial Intelligence 62 (1993) 41-78. Best-first search is a general heuristic search algorithm that always expands next a frontier node of lowest cost. It includes as special cases breadth-first search, Dijkstra's single-source shortest-path algorithm, and the A* algorithm. Its applicability, however, is(More)
Recently, best-first search algorithms have been introduced that store their nodes on disk, to avoid their inherent memory limitation. We introduce several improvements to the best of these, including parallel processing, to reduce their storage and time requirements. We also present a linear-time algorithm for bijectively mapping permutations to integers(More)
We describe a new technique for designing more accurate admis sible heuristic evaluation functions based on pattern databases While many heuristics such as Manhattan distance compute the cost of solving individual subgoals independently pattern databases consider the cost of solving multiple subgoals simultaneously Exist ing work on pattern databases allows(More)
We consider the case of heuristic search where the location of the goal may change during the course of the search. For example, the goal may be a target that is actively avoiding the problem solver. We present a moving target search algorithm (MTS) to solve this problem. We prove that if the average speed of the target is slower than that of the problem(More)