Genetic Programming of Control Knowledge for Planning

Proceedings of the 4th International Conference on Artificial Intelligence Planning Systems, AIPS 1998

Volumn , Issue , 1998, Pages 137-143

  Aler, Ricardo ^a   Borrajo, Daniel ^a   Isasi, Pedro ^a  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

DOMAIN KNOWLEDGE; GENETIC ALGORITHMS; QUALITY CONTROL;

COMPLEX DOMAINS; CONTROL-RULES; DOMAIN KNOWLEDGE; DOMAIN-INDEPENDENT PLANNER; NON TRIVIAL PROBLEMS; SIMPLE APPROACH; TWO DOMAINS;

GENETIC PROGRAMMING;

EID: 35248850587     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Ricardo Aler,Daniel Borrajo, andPedroIsasi. Evolvingheuristics for planning. In Proceedings of the Beventh Annual Conferenceon Evolutionary Programming, Lecture Notes in Artificial Intelligence, San Diego, CA,March1998. Springer-Verlag.
2. Daniel Borrajo and ManuelaVeloso. Lazy incremental learning of control knowledgefor efficiently obtaining quality plans. AI Review Journal. Special Issue on Lazy Learning, 11(1-5):371-405, February 1997.
3. Jaime G. Carboneil, Craig A. Knoblock, and Steven Minton.Prodigy: Anintegrated architecture for plan- . ning and learning. In K. VanLehn, editor, Architectures for Intelligence. Erlbaum, Hillsdale, NJ, 1990. Also Technical Report CMU-CS-89.189.
4. David Chapman.Planning for conjunctive goals. Artificial Intelhgenc,32:333-378, 1987.
5. Tara A. Estlin and RaymondMooney.Hybrid learning of search control for partial-order planning. In Nero Directions in AI Planning, pages 115-128. IOSPress, 1996.
6. J.H. Holland.Adaptationin NaturalandArtificial Systems. TheUniversity of MichiganPress, 1975.
7. Subbarao Kambhampati. Flexible Reuse and Modification in HierarchicalPlanning: A Validation Structu Based Approach PhDthesis, ComputerVision Laboratory, Center for AutomationResearch, University of Maryland, College Park, MD,1989.
8. Suresh Katukamand Subbarao Kambharnpati. Learning explanation-basedsearch control rules for partial order planning. In Proceedingsof theTwelfth National Conferenceon Artificial Intelligence, pages 582-587 Seattle, WA,1994. AAAI Press.
9. J. R. Koza. Genetic Programming:Onthe Programmingof Computersby Meansof Natural Selection. MIT Press, Cambridge,MA,1992.
10. C. Leckie and I. Zukerman.Learning search control rules for planning: Aninductive approach.In Proceedings of the Eighth International WorkshoponMachine Learning, pages 422-426, Evanston, IL, 1991. Morgan Kanfmann.
11. Steven Minton, Craig A. Knoblock, Dan R. Kuokka, Yolanda Gil, Robert L. Joseph, and Jalme G. Carbonell. PRODIOY 2.0: The manualand tutorial. Technical Report CMU-CS-89.146, School of Computer Science, Carnegie MellonUniversity, 1989.
12. Steven Minton and Monte Zweben. Learning, planning and scheduling: Anoverview. In Steven Minton, editor, MachineLearningMethodsfor Planning, chapter 8. MorganKanfmann,1993.
13. P. Stone, M.Veloso,and J. Blythe. Theneed for ditferent domain-independentheuristics. In Proceedingsof the Second International Conference on AI Planning 3ystems., pages 164-169.
14. Walter Alden Tackett. Recombination, Selection, and the Genetic Construction of ComputerPrograms. PhD thesis, University of Southern California, Departmen of Electrical EngineeringSystems, 1994.
15. M.M.Veloso.Nonlinearproblemsolving using intelligent casual-commitment. Technical Report CMU-CS-89-210, School of ComputerScience, Carnegie Mellon University, 1989.
16. ManuelaVeloso, JalmeCarbonell, Alicia Pdrez, Daniel Borrajo, Eugene Fink, and Jim Blythe. Integrating planning and learning: ThePRODIGY architecture. Journalof EzperimentalandTheoretical AI, 7:81-120, 1995.
17. Manuela M. Veloso and Jime G. Carbonell. Derivational analogy in PRODIGY: Automating case acquisition, storage, and utilization. MachineLearning, 10(3):249-278, March1993.