Titlebook: Local Search for Planning and Scheduling; ECAI 2000 Workshop, Alexander Nareyek Conference proceedings 2001 Springer-Verlag Berlin Heidelb

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scrape

Incremental Local Search for Planning Problemsn plan, the third stage guarantees the completeness and optimality of the generated solution and the fourth stage, a partial-order planner, completes the process by finding the missing actions of the final solution plan, if any.

bromide

HARD

Empirical Evaluation of Local Search Methods for Adapting Planning Policies in a Stochastic Environmthe heuristic sets and show that the two chosen local search algorithms perform well. We present empirical data that suggests this is due to smoothness properties of the search space for the search algorithms.

謙虛的人

The GRT Planner: New Resultsnd automatic irrelevant objects elimination, are presented. Finally, the planner has been equipped with a hill-climbing strategy and a closed list of visited states for pruning purposes. Performance results show that GRT exhibits significant improvement over its AIPS-00 competition version.

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Alexander NareyekIncludes supplementary material:

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Meta-heuristics: The State of the Arteering, economics and science in reasonable time horizons. In this paper we give some insight into the state of the art of meta-heuristics. This primarily focuses on the significant progress which general frames within the meta-heuristics field have implied for solving combinatorial optimization problems, mainly those for planning and scheduling.

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