Utilizing Problem Structure in Planning: A Local Search Approach (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence) par Jorg Hoffmann: New Paperback (2004)

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N� de r�f. du vendeur 6666-IUK-9783540202592

D�tails bibliographiques

Titre: Utilizing Problem Structure in Planning: A Local Search Approach (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
�diteur: Springer 2004-01-12
Date de parution: 2004
Langue: anglais
ISBN � 10 chiffres: 3540202595
ISBN � 13 chiffres: 9783540202592
Reliure: Paperback
�tat de l'article: New

A propos de ce titre

Synopsis

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.

Pr�sentation de l'�diteur

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.

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