Building and Refining Abstract Planning Cases by Change of Representation Language

TL;DR

This paper introduces a novel abstraction method for planning cases that involves changing the representation language, enabling more effective case learning and reasoning in complex domains.

Contribution

It presents a new abstraction methodology and a sound, complete learning algorithm for changing representation languages in planning cases, improving over traditional sentence-dropping methods.

Findings

Significant advantages of abstract case reasoning over classical hierarchical planning

Development of a system called Paris for automatic learning of abstract planning cases

Empirical validation in mechanical engineering process planning domain

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.