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Title: HAPrc: An Automatically Configurable Planning System
Author(s): D. Vrakas, G. Tsoumakas, N. Bassiliades, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (44 pages).
Appeared in: AI Communications, 18 (1), pp. 41-60, 2005.
Abstract: This paper presents an adaptive planning system, called HAPRC, which automatically fine-tunes its planning parameters according to the morphology of the problem in hand, through a combination of Planning, Machine Learning and Knowledge-Based techniques. The adaptation is guided by a rule-based system that sets planner configuration parameters based on measurable characteristics of the problem instance. The knowledge of the rule system has been acquired through a rule induction algorithm. Specifically, the approach of propositional rule learning was applied to a dataset produced by results from experiments on a large number of problems from various domains, including those used in the three International Planning Competitions. The improvement of the adaptive system over the original planner is assessed through thorough experiments in problems of both known and unknown domains.
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        This paper has been cited by the following:

1 A. D. Mali, M. Tang, “State-space planning with variants of A*”, International Journal on Artificial Intelligence Tools, 15 (3), 2006, pp. 433-464.
2 O. Hatzi, "Web Service Composition through AI Planning", PhD Thesis, Department of Informatics and Telematics, Harokopeio University, 2009
3 S. Jimenez, T. del Rosa, S. Fernandez, F. Fernandez, D. Borrajo, "A Review of Machine Learning for Automated Planning", Knowledge Engineering Review, 27(4), pp. 433-467, 2012.