Incremental and Non-incremental Learning of Control Knowledge ...

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Discussion More recent and future work 36 Mixed initiative Effects of knowledge representation Effects of prior knowledge Learning for multiple criteria Learning for HTN+POP planners Using numerical predicates on conditions of control rules Active learning: on-line generation of appropriate training problems Learning for planning and scheduling Learning in more recent problem solvers
37 Referencias [Aler et al., 2002] Ricardo Aler, Daniel Borrajo, and Pedro Isasi. Using genetic programming to learn and improve control knowledge. Artificial Intelligence, 141(1-2):29–56, October 2002. [Ambite et al., 2000] José Luis Ambite, Craig A. Knoblock, and Steven Minton. Learning plan rewriting rules. In Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling, pages 14–17, Breckenbridge, CO (USA), April 2000. [Bhatnagar, 1992] Neeraj Bhatnagar. On-line learning from search failures. PhD thesis, Rutgers University, 1992. [Borrajo and Veloso, 1997] Daniel Borrajo and Manuela Veloso. Lazy incremental learning of control knowledge for efficiently obtaining quality plans. AI Review Journal. Special Issue on Lazy Learning, 11(1-5):371–405, February 1997. Also in the book ”Lazy Learning”, David Aha (ed.), Kluwer Academic Publishers, May 1997, ISBN 0-7923-4584-3. [Carbonell and Gil, 1990] Jaime G. Carbonell and Yolanda Gil. Learning by experimentation: The operator refinement method. In R. S. Michalski and Y. Kodratoff, editors, Machine Learning: An Artificial Intelligence Approach, Volume III, pages 191–213. Morgan Kaufmann, Palo Alto, CA, 1990. [Estlin and Mooney, 1995] Tara A. Estlin and Raymond Mooney. Hybrid learning of search control for partial order planning. Technical report, University of Texas, 1995.
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Page 3 and 4: Motivation Motivation for hamlet 3
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Page 43 and 44: Discussion More related work 29 Non
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