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010   $a3-540-39607-1
024 7 $a10.1007/b93903
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035   $a(PQKBTitleCode)TC0000327517
035   $a(PQKBWorkID)10301908
035   $a(PQKB)11374831
035   $a(DE-He213)978-3-540-39607-9
035   $a(MiAaPQ)EBC3087791
035   $a(PPN)155174134
035   $a(EXLCZ)991000000000212231
100   $a20121227d2003      u|             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aUtilizing Problem Structure in Planning$b[electronic resource] $eA Local Search Approach /$fby Jörg Hoffmann
205   $a1st ed. 2003.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2003.
215   $a1 online resource (XVIII, 254 p.) 
225 1 $aLecture Notes in Artificial Intelligence ;$v2854
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-540-20259-5 
320   $aIncludes bibliographical references and index.
327   $aPlanning: Motivation, Definitions, Methodology -- 1: Introduction -- 2: Planning -- A Local Search Approach -- 3: Base Architecture -- 4: Dead Ends -- 5: Goal Orderings -- 6: The AIPS-2000 Competition -- Local Search Topology -- 7: Gathering Insights -- 8: Verifying the h?+? Hypotheses -- 9: Supporting the hFF Hypotheses -- 10: Discussion -- Appendix A: Formalized Benchmark Domains -- Appendix B: Automated Instance Generation.
330   $aPlanning is a crucial skill for any autonomous agent, be it a physically embedded agent, such as a robot, or a purely simulated software agent. For this reason, planning, as a central research area of artificial intelligence from its beginnings, has gained even more attention and importance recently. After giving a general introduction to AI planning, the book describes and carefully evaluates the algorithmic techniques used in fast-forward planning systems (FF), demonstrating their excellent performance in many wellknown benchmark domains. In advance, an original and detailed investigation identifies the main patterns of structure which cause the performance of FF, categorizing planning domains in a taxonomy of different classes with respect to their aptitude for being solved by heuristic approaches, such as FF. As shown, the majority of the planning benchmark domains lie in classes which are easy to solve.
410  0$aLecture Notes in Artificial Intelligence ;$v2854
606   $aArtificial intelligence
606   $aAlgorithms
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021
615  0$aArtificial intelligence.
615  0$aAlgorithms.
615 14$aArtificial Intelligence.
615 24$aAlgorithm Analysis and Problem Complexity.
676   $a006.3/33
700   $aHoffmann$b Jörg$4aut$4http://id.loc.gov/vocabulary/relators/aut$0564758
906   $aBOOK
912   $a996465795203316
996   $aUtilizing problem structure in planning$9955105
997   $aUNISA