Sokoban: Enhancing general single-agent search methods using domain knowledge
نویسندگان
چکیده
منابع مشابه
Sokoban: Enhancing general single-agent search methods using domain knowledge
Artificial intelligence (AI) research has developed an extensive collection of methods to solve state-space problems. Using the challenging domain of Sokoban, this paper studies the effect of general search enhancements on program performance. We show that the current state of the art in AI generally requires a large research and programming effort to use domain-dependent knowledge to solve eve...
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Al research has developed an extensive collect ion of methods to solve state-space problems. Using the challenging domain of Sokoban, this paper studies the effect of search enhancements on program performance. We show that the current state of the ar t in AT generally requires a large p rog ramming and research effort into domain-dependent: methods to solve even moderately complex problems in ...
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Single-agent search is a powerful tool for solving a variety of applications. Most of the academic application domains used to explore single-agent search techniques have the property that if you start with a solvable state, at no time in the search can you reach a state that is unsolvable (it may, however, not be minimal). In this paper we address the implications that arise when states in the...
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Humans can eeectively navigate through large search spaces, enabling them to solve problems with daunting complexity. This is largely due to an ability to successfully distinguish between relevant and irrelevant actions (moves). In this paper we present a new single-agent search pruning technique that is based on a move's innuence. The innuence measure is a crude form of relevance in that it is...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2001
ISSN: 0004-3702
DOI: 10.1016/s0004-3702(01)00109-6