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Acquiring planning domain models using LOCM

Published online by Cambridge University Press:  22 February 2013

Stephen N. Cresswell ,
Thomas L. McCluskey  and
Margaret M. West
Stephen N. Cresswell
Affiliation:
The Stationery Office, St. Crispins, Duke Street, Norwich NR3 1PD, UK; e-mail: stephen.cresswell@tso.co.uk
Thomas L. McCluskey
Affiliation:
School of Computing and Engineering, The University of Huddersfield, Huddersfield HD1 3DH, UK; e-mail: t.l.mccluskey@hud.ac.uk, m.m.west@hud.ac.uk
Margaret M. West
Affiliation:
School of Computing and Engineering, The University of Huddersfield, Huddersfield HD1 3DH, UK; e-mail: t.l.mccluskey@hud.ac.uk, m.m.west@hud.ac.uk
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Abstract

The problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for artificial intelligence (AI) planning. This paper describes Learning Object-Centred Models (LOCM), a system that carries out the automated generation of a planning domain model from example training plans. The novelty of LOCM is that it can induce action schema without being provided with any information about predicates or initial, goal or intermediate state descriptions for the example action sequences. Each plan is assumed to be a sound sequence of actions; each action in a plan is stated as a name and a list of objects that the action refers to. LOCM exploits assumptions about the kinds of domain model it has to generate, rather than handcrafted clues or planner-oriented knowledge. It assumes that actions change the state of objects, and require objects to be in a certain state before they can be executed. In this paper, we describe the implemented LOCM algorithm, the assumptions that it is based on, and an evaluation using plans generated through goal-directed solutions, through random walk, and through logging human-generated plans for the game of freecell. We analyze the performance of LOCM by its application to the induction of domain models from five domains.

Information

Type
Articles
Information
The Knowledge Engineering Review , Volume 28 , Special Issue 2: Knowledge Engineering for Planning and Scheduling , June 2013 , pp. 195 - 213
Copyright
Copyright © Cambridge University Press 2013 

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