000 | 03170nam a22005415i 4500 | ||
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001 | 978-3-540-39607-9 | ||
003 | DE-He213 | ||
005 | 20240423132548.0 | ||
007 | cr nn 008mamaa | ||
008 | 121227s2003 gw | s |||| 0|eng d | ||
020 |
_a9783540396079 _9978-3-540-39607-9 |
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024 | 7 |
_a10.1007/b93903 _2doi |
|
050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
|
072 | 7 |
_aCOM004000 _2bisacsh |
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072 | 7 |
_aUYQ _2thema |
|
082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aHoffmann, Jörg. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aUtilizing Problem Structure in Planning _h[electronic resource] : _bA Local Search Approach / _cby Jörg Hoffmann. |
250 | _a1st ed. 2003. | ||
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2003. |
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300 |
_aXVIII, 254 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v2854 |
|
505 | 0 | _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. | |
520 | _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. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aAlgorithms. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aAlgorithms. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783540202592 |
776 | 0 | 8 |
_iPrinted edition: _z9783662182208 |
830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v2854 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/b93903 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
912 | _aZDB-2-LNC | ||
912 | _aZDB-2-BAE | ||
942 | _cSPRINGER | ||
999 |
_c189150 _d189150 |