Titlebook: Dynamic Flexible Constraint Satisfaction and its Application to AI Planning; Ian Miguel Book 2004 Springer Verlag London Limited 2004 Cons

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Expertenwissen für Bildungsreformenexible Local Changes algorithms presented in the previous chapter. Due to the size of the study (almost 40,000 individual problems solved tens of times each), full results are presented separately in Appendix C. In this chapter, therefore, a selection has been made to show graphically and in tables

onlooker

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Evidenzbasierte P?dagogik – ein Déjà-vu?udy is not to show that GP-.DCSP is a vast improvement over the current state of the art, although in some of the tests excellent results are obtained, but to establish this planner as a solid platform upon which to build a fully flexible planner in the subsequent chapters.

Intact

Einleitung: Wirtschaftliche Rationalit?treal problems. This is due to the fact that it too is founded on hard constraints, and hence has no capability of making compromises. In order to address this issue, a new . is defined which is designed to allow a tradeoff between the length of a plan and the number of compromise decisions it contai

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Einleitung: Wirtschaftliche Rationalit?tn plans of three different satisfaction degrees at different distributions of plan length. The results for this test allow an examination of the relationship between solution distribution and difficulty of plan synthesis. Boolean versions of the twelve problems are also solved in order to investigat

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Einleitung: Wirtschaftliche Rationalit?teveloped which have been suggested throughout are then enumerated and preliminary suggestions as to their execution are made. Finally, a number of potential application areas for dynamic flexible CSP are discussed as well as how the novel search structure employed by GP-.DCSP and FGP might be utilis