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Building and Refining Abstract Planning Cases by Change of Representation Language
Ralph Bergmann,Wolfgang Wilke +1 more
TLDR
In this article, the authors propose a more general view of abstraction involving the change of representation language, which is one of the most promising approaches to improve the performance of problem solvers.Abstract:
ion 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. We have 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.read more
Citations
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Quantitative Results Concerning the Utility of Explanation-Based Learning
Ashwin Ram,David B. Leake +1 more
Entwurf, Implementierung und experimentelle Bewertung von Auswahlverfahren für abstrakte Pläne in dem fallbasiertem Planungssystem PARIS
TL;DR: In this paper, a konkrete Problemstellung with ihrer Losung aus der kon-kreten Planungswelt in eine abstraktere Planungsswelt durch eine Abstraktion transformieren is presented.
Learning Domain Structure in HGNs for Nondeterministic planning
TL;DR: This paper presents preliminary ideas of the work for automated learning of Hierarchical Goal Networks in nondeterministic domains and is currently implementing the ideas expressed.
References
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Book ChapterDOI
Learning Plan Abstractions
TL;DR: A formal model and a method are described for learning abstract plans from concrete plans and deductively justified abstractions are constructed which are tailored to the application domain described by the theory.