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The need for different domain-independent heuristics.
Peter Stone,
Manuela Veloso, and Jim Blythe.
In Proceedings
of the Second International Conference on AI Planning Systems, pp. 164–169, June 1994.
[PDF]146.6kB [postscript]125.5kB
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.
@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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