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Building and refining abstract planning cases by change of representation language (1995)
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| Venue: | JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH |
| Citations: | 57 - 7 self |
BibTeX
@ARTICLE{Bergmann95buildingand,
author = {Ralph Bergmann and Wolfgang Wilke},
title = {Building and refining abstract planning cases by change of representation language},
journal = {JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH},
year = {1995},
volume = {3},
pages = {53--118}
}
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Abstract
Abstraction is one of the most promising approaches to improve the performance of problem solvers. In several domains abstraction by dropping sentences of a domain description -- as used in most hierarchical planners -- has proven useful. In this paper we present examples which illustrate significant drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. Wehave developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract. However, to achieve a powerful change of the representation language, the abstract language itself as well as rules which describe admissible ways of abstracting states must be provided in the domain model. This new abstraction approach is the core of Paris (Plan Abstraction and Refinement in an Integrated System), a system in which abstract planning cases are automatically learned from given concrete cases. An empirical study in the domain of process planning in mechanical engineering shows significant advantages of the proposed reasoning from abstract cases over classical hierarchical planning.
Keyphrases
representation language refining abstract planning case plan abstraction integrated system general view abstract language abstract planning case classical hierarchical planning mechanical engineering show significant advantage several domain abstraction complete change empirical study significant drawback complete learning algorithm process planning domain description new abstraction methodology problem solver new abstraction approach powerful change related sound abstract case domain model promising approach admissible way concrete case hierarchical planner
