Action Knowledge Acquisition with Opmaker2

McCluskey, T.L., Cresswell, S.N., Richardson, N.E. and West, Margaret M. (2010) Action Knowledge Acquisition with Opmaker2. In: Agents and Artificial Intelligence. Communications in Computer and Information Science, 67 . Springer, pp. 137-150. ISBN 978-3-642-11818-0

PDF - Published Version Restricted to Repository staff only Download (137kB)

Abstract

AI planning engines require detailed specifications of dynamic knowledge of the domain in which they are to operate, before they can function. Further, they require domain-specific heuristics before they can function efficiently. The problem of formulating domain models containing dynamic knowledge regarding actions is a barrier to the widespread uptake of AI planning, because of the difficulty in acquiring and maintaining them. Here we postulate a method which inputs a partial domain model (one without knowledge of domain actions) and training solution sequences to planning tasks, and outputs the full domain model, including heuristics that can be used to make plan generation more efficient.

To do this we extend GIPO’s Opmaker system [1] so that it can induce representations of actions from training sequences without intermediate state information and without requiring large numbers of examples. This method shows the potential for considerably reducing the burden of knowledge engineering, in that it would be possible to embed the method into an autonomous program (agent) which is required to do planning. We illustrate the algorithm as part of an overall method to acquire a planning domain model, and detail results that show the efficacy of the induced model.

Item Type:	Book Chapter
Additional Information:	International Conference, ICAART 2009, Porto, Portugal, January 19-21, 2009. Revised Selected Papers
Uncontrolled Keywords:	Planning and Scheduling; Machine Learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools:	School of Computing and Engineering School of Computing and Engineering > High-Performance Intelligent Computing School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge School of Computing and Engineering > Pedagogical Research Group
Related URLs:	Publisher Author Author
Depositing User:	Thomas Leo Mccluskey
Date Deposited:	19 May 2010 15:01
Last Modified:	28 Aug 2021 22:16
URI:	http://eprints.hud.ac.uk/id/eprint/7716

Downloads

Repository Staff Only: item control page

View Item