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Permanent link (DOI): https://doi.org/10.7939/R3N010080

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Improving Local Search for Resource-Constrained Planning Open Access

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Author or creator
Nakhost, Hootan
Hoffman, Joerg
Mueller, Martin
Additional contributors
Subject/Keyword
Resource-constrained planning
Artificial Intelligence
Local search
Planning
Type of item
Computing Science Technical Report
Computing science technical report ID
TR10-02
Language
English
Place
Time
Description
Technical report TR10-02. A ubiquitous feature of planning problems -- problems involving the automatic generation of action sequences for attaining a given goal -- is the need to economize limited resources such as fuel or money. While heuristic search, mostly based on standard algorithms such as A*, is currently the superior method for most varieties of planning, its ability to solve critically resource-constrained problems is limited: current planning heuristics are bad at dealing with this kind of structure. To address this, one can try to devise better heuristics. An alternative approach is to change the nature of the search instead. Local search has received some attention in planning, but not with a specific focus on how to deal with limited resources. We herein begin to fill this gap. We highlight the limitations of previous methods, and we devise a new improvement (smart restarts) to the local search method of a previously proposed planner (Arvand). Systematic experiments show how performance depends on problem structure and search parameters. In particular, we show that our new method can outperform previous planners by a large margin.
Date created
2010
DOI
doi:10.7939/R3N010080
License information
Creative Commons Attribution 3.0 Unported
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