Improving Control-Knowledge Acquisition for Planning by Active Learning
Automatically acquiring control-knowledge for planning, as it is the case for Machine Learning in general, strongly depends on the training examples. In the case of planning, examples are usually extracted from the search tree generated when solving problems. Therefore, examples depend on the problems used for training. Traditionally, these problems are randomly generated by selecting some difficulty parameters. In this paper, we discuss several active learning schemes that improve the relationship between the number of problems generated and planning results in another test set of problems. Results show that these schemes are quite useful for increasing the number of solved problems.
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- 2.Liere, R., Tadepalli, P., Hall, D.: Active learning with committees for text categorization. In: Proceedings of the Fourteenth Conference on Artificial Intelligence, Providence, RI, USA, pp. 591–596 (1997)Google Scholar
- 4.Mitchell, T.M., Utgoff, P.E., Banerji, R.B.: Learning by experimentation: Acquiring and refining problem-solving heuristics. In: Machine Learning, An Artificial Intelligence Approach. Tioga Press, Palo Alto (1983)Google Scholar
- 5.Bryant, C., Muggleton, S., Oliver, S., Kell, D., Reiser, P., King, R.: Combining inductive logic programming, active learning and robotics to discover the function of genes. Linkoping Electronic Articles in Computer and Information Science 6(12) (2001)Google Scholar
- 6.Fedorov, V.: Theory of Optimal Experiments. Academic Press, London (1972)Google Scholar
- 7.Zimmerman, T., Kambhampati, S.: Learning-assisted automated planning: Looking back, taking stock, going forward. AI Magazine 24(2), 73–96 (2003)Google Scholar
- 8.Minton, S.: Learning Effective Search Control Knowledge: An Explanation-Based Approach. Kluwer Academic Publishers, Boston (1988)Google Scholar
- 10.Borrajo, D., Veloso, M.: Lazy incremental learning of control knowledge for efficiently obtaining quality plans. AI Review Journal. Special Issue on Lazy Learning 11(1-5), 371–405 (1997), also in: Aha, D. (ed.) Lazy Learning. Kluwer Academic Publishers, Dordrecht (1997)Google Scholar
- 11.Rodríguez-Moreno, M.D., Borrajo, D., Cesta, A., Oddi, A.: Integrating planning and scheduling in workflow domains. Expert System with Applications 33(2) (2007)Google Scholar
- 14.Fox, M., Long, D.: The automatic inference of state invariants in tim. Journal of Artificial Intelligence Research 9, 317–371 (1998)Google Scholar
- 15.Fox, M., Long, D.: PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. University of Durham, Durham (UK) (2002)Google Scholar