Peter Stone's Selected Publications

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The need for different domain-independent heuristics

Peter Stone, Manuela Veloso, and Jim Blythe. The need for different domain-independent heuristics. In Proceedings of the Second International Conference on AI Planning Systems, pp. 164–169, June 1994.

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Abstract

PRODIGY's planning algorithm uses domain-independent search heuristics. In this paper, we support our belief that there is no single search heuristic that performs more efficiently than others for all problems or in all domains. The paper presents three different domain-independent search heuristics of increasing complexity. We run PRODIGY with these heuristics in a series of artificial domains where in fact one of the heuristics performs more efficiently than the others. However, we introduce an additional simple domain where the apparently worst heuristic outperforms the other two. The results we obtained in our empirical experiments lead to the main conclusion of this paper: planning algorithms need to use different search heuristics in different domains. We conclude the paper by advocating the need to learn the correspondence between particular domain characteristics and specific search heuristics for planning efficiently in complex domains.

BibTeX Entry

@InProceedings(StoVelBly94, Author="Peter Stone and Manuela Veloso and Jim Blythe",
        Title="The need for different domain-independent heuristics",
        Booktitle="Proceedings of the Second International Conference on {AI} Planning Systems",
        pages="164--169",
        Year="1994", Month="June",
        abstract={
                  PRODIGY's planning algorithm uses domain-independent
                  search heuristics.  In this paper, we support our
                  belief that there is no single search heuristic that
                  performs more efficiently than others for all
                  problems or in all domains.  The paper presents
                  three different domain-independent search heuristics
                  of increasing complexity. We run PRODIGY with these
                  heuristics in a series of artificial domains where
                  in fact one of the heuristics performs more
                  efficiently than the others.  However, we introduce
                  an additional simple domain where the apparently
                  worst heuristic outperforms the other two.  The
                  results we obtained in our empirical experiments
                  lead to the main conclusion of this paper: planning
                  algorithms need to use different search heuristics
                  in different domains.  We conclude the paper by
                  advocating the need to learn the correspondence
                  between particular domain characteristics and
                  specific search heuristics for planning efficiently
                  in complex domains.
        },
)

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