Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Mahanti, AmbujSubject: search.filters.subject.search

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    On the Asymptotic Performance of IDA*
    (1995) Mahanti, Ambuj; Ghosh, Subrata; Nau, D.S.; Pal, A.K.; Kanal, L.N.; ISR
    Since best-first search algorithms such as A* require large amounts of memory, they sometimes cannot run to completion, even on problem instances of moderate size. This problem has led to the development of limited-memory search algorithms, of which the best known is IDA* [9, 10]. This paper presents the following results about IDA* and related algorithms: The analysis of asymptotic optimality for IDA* in [10] is incorrect. There are trees satisfying the asymptotic optimality conditions given in [10] for which IDA* is not asymptotically optimal.

    To correct the above problem, we state and prove necessary and sufficient for asymptotic optimality of IDA* on trees. On trees not satisfying our conditions, we show that no best-first limited- memory search algorithm can be asymptotically optimal.

    On graphs, IDA* can perform quite poorly. In particular, there are graphs on which IDA* does (22N) node expansions where N is the number of nodes expanded by A*.

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    PRA: Massively Parallel Heuristic Search
    (1991) Evett, Matthew; Hendler, James A.; Mahanti, Ambuj; Nau, D.; ISR
    In this paper we describe a variant of A* search designed to run on the massively parallel, SIMD Connection Machine. The algorithm is designed to run in a limited memory by use of a retraction technique which allows nodes with poor heuristic values to be removed from the open list, until such time as they may need reexpansion, more promising paths having failed. Our algorithm, called PRA* (for Parallel Retraction A*), is designed to maximize use of the Connection Machine's memory and processors. In addition, the algorithm is guaranteed to return an optimal path when an admissable heuristic is used. Results comparing PRA* to Korf's IDA* for the fifteen-puzzle show significantly fewer node expansions for PRA*. In addition, empirical results show significant parallel speedups, indicative of the algorithm's design for high processor utilization.