Learning macro-actions for arbitrary planners and domains

ICAPS 2007, 17th International Conference on Automated Planning and Scheduling

Volumn , Issue , 2007, Pages 256-263

  Newton, M A Hakim ^a   Levine, John ^a   Fox, Maria ^a   Long, Derek ^a  

^a UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS; LEARNING SYSTEMS; PLANNING;

COMPLEX DOMAINS; LARGE PROBLEMS; LEARNING METHODS; MACRO LEARNING; OFFLINE; PROPERTY; RE-ENGINEERING; RECENT PROGRESS; SIMPLE++; STRUCTURAL KNOWLEDGE;

GENETIC ALGORITHMS;

EID: 58349093081     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Aler, R.; Borrajo, D.; and Isasi, P. 2001. Learning to solve problems efficiently by means of genetic programming. Evolutionary Computation 9(4):387-420.
2. Armano, G.; Cherchi, G.; and Vargiu, E. 2005. A system for generating macro-operators from static domain analysis. In Proceeding (453) Artificial Intelligence and Applications.
3. Borrajo, D., and Veloso, M. 1997. Lazy incremental learning of control knowledge for efficiently obtaining quality plans. AI Review 11(1-5):371-405.
4. Botea, A.; Enzenberger, M.; Müller, M.; and Schaeffer, J. 2005. Macro-FF: Improving AI planning with automatically learned macro-operators. Journal of Artificial Intelligence Research 24:581-621.
5. Coles, A., and Smith, A. 2004. MARVIN: Macro-actions from reduced versions of the instance. In IPC4 Booklet. ICAPS.
6. Dawson, C., and Siklóssy, L. 1977. The role of preprocessing in problem solving systems. In Proceedings of the International Joint Conference on Artificial Intelligence, 465-471.
7. Fikes, R. E.; Hart, P. E.; and Nilsson, N. J. 1972. Learning and executing generalized robot plans. Artificial Intelligence 3(4):251-288.
8. Hernádvölgyi, I. 2001. Searching for macro operators with automatically generated heuristics. In Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence; Advances in Artificial Intelligence, 194-203.
9. Hoffmann, J., and Nebel, B. 2001. The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14:253-302.
10. Iba, G. A. 1989. A heuristic approach to the discovery of macrooperators. Machine Learning 3:285-317.
11. Korf, R. E. 1985. Macro-operators: A weak method for learning. Artificial Intelligence 26:35-77.
12. Levine, J., and Humphreys, D. 2003. Learning action strategies for planning domains using genetic programming. In Applications of Evolutionary Computing, EvoWorkshops2003, volume 2611, 684-695.
13. Minton, S. 1985. Selectively generalising plans for problemsolving. In Proceedings of the International Joint Conference on Artificial Intelligence.
14. Muslea, I. 1998. A general purpose AI planning system based on the genetic programming paradigm. In Proceedings of the World Automation Congress.
15. Spector, L. 1994. Genetic programming and AI planning system. In Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI-94, 1329-1334.
16. Veloso, M.; Carbonell, J.; Perez, A.; Borrajo, D.; Fink, E.; and Blythe, J. 1995. Integrating planning and learning: The PRODIGY architecture. Journal of Experimental and Theoretical Artificial Intelligence 7:81-120.
17. Westerberg, C. H., and Levine, J. 2000. GenPlan: Combining genetic programming and planning. In 19th Workshop of the UK Planning and Scheduling Special Interest Group (PLANSIG).
18. Westerberg, C. H., and Levine, J. 2001. Optimising plans using genetic programming. In 6th European Conference on Planning.