Acquisition of Object-Centred Domain Models from Planning Examples

S. N. Cresswell, T. L. McCluskey, M. M. West

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

43 Citations (Scopus)


The problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for AI Planning. This paper describes LOCM, a system which carries out the automated induction of action schema from sets of example plans. 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 the assumption that actions change the state of objects, and require objects to be in a certain state before they can be executed. 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. In this paper we describe the implemented LOCM algorithm, and analyse its performance by its application to the induction of domain models for several domains. To evaluate the algorithm, we used random action sequences from existing models of domains, as well as solutions to past IPC problems.

Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Automated Planning and Scheduling
Subtitle of host publicationICAPS 2009
EditorsAlfonso Gerevini, Adele Howe, Amedeo Cesta, Ioannis Refanidis
PublisherAAAI press
Number of pages4
ISBN (Electronic)9781577354079
ISBN (Print)9781577354062
Publication statusPublished - Sep 2009
Event19th International Conference on Automated Planning and Scheduling - Thessaloniki, Greece
Duration: 19 Sep 200923 Sep 2009
Conference number: 19 (Link to Conference Website)


Conference19th International Conference on Automated Planning and Scheduling
Abbreviated titleICAPS 2009
Internet address


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