Utilizing Problem Structure in Planning A Local Search Approach /
Hoffmann, Jörg.
Utilizing Problem Structure in Planning A Local Search Approach / [electronic resource] : by Jörg Hoffmann. - 1st ed. 2003. - XVIII, 254 p. online resource. - Lecture Notes in Artificial Intelligence, 2854 2945-9141 ; . - Lecture Notes in Artificial Intelligence, 2854 .
Planning: 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.
Planning 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.
9783540396079
10.1007/b93903 doi
Artificial intelligence.
Algorithms.
Artificial Intelligence.
Algorithms.
Q334-342 TA347.A78
006.3
Utilizing Problem Structure in Planning A Local Search Approach / [electronic resource] : by Jörg Hoffmann. - 1st ed. 2003. - XVIII, 254 p. online resource. - Lecture Notes in Artificial Intelligence, 2854 2945-9141 ; . - Lecture Notes in Artificial Intelligence, 2854 .
Planning: 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.
Planning 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.
9783540396079
10.1007/b93903 doi
Artificial intelligence.
Algorithms.
Artificial Intelligence.
Algorithms.
Q334-342 TA347.A78
006.3