DISTILL: Learning Domain-Specific Planners by Example

Proceedings, Twentieth International Conference on Machine Learning

Volumn 2, Issue , 2003, Pages 800-807

  Winner, Elly ^a   Veloso, Manuela ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISTILL ALGORITHMS; DOMAIN-INDEPENDENT PLANNING; DOMAIN-SPECIFIC PLANNING;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATABASE SYSTEMS; INTELLIGENT AGENTS; PLANNING; PROBLEM SOLVING; SUBROUTINES;

LEARNING SYSTEMS;

EID: 1942420378     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Bauer, M. (1979). Programming by examples. Artificial Intelligence, 12, 1-21.
2. Carbonell, J. G., & Gil, Y. (1990). Learning by experimentation: The operator refinement method. In R. S. Michalski and Y. Kodratoff (Eds.), Machine learning: An artificial intelligence approach, volume III, 191-213. Palo Alto, CA: Morgan Kaufmann.
3. Cimatti, A., Roveri, M., & Traverso, P. (1998). Automatic OBDD-based generation of universal plans in non-deterministic domains. Proceedings of the 15th National Conference on Artificial Intelligence (AAAI'98) (pp. 875-881). AAAI Press.
4. Fikes, R. E., Hart, P. E., & Nilsson, N. J. (1972). Learning and executing generalized robot plans. Artificial Intelligence, 3, 251-288.
5. Friedland, P. E., & Iwasaki, Y. (1985). The concept and implementation of skeletal plans. Journal of Automated Reasoning, 1, 161-208.
6. Kambhampati, S., & Hendler, J. A. (1992). A validation-structure- based theory of plan modification and reuse. Artificial Intelligence, 55, 193-258.
7. Khardon, R. (1999). Learning action strategies for planning domains. Artificial Intelligence, 113, 125-148.
8. Lau, T. (2001). Programming by demonstration: a machine learning approach. Doctoral dissertation, University of Washington, Seattle.
9. Minton, S. (1988). Learning effective search control knowledge: An explanation-based approach. Boston, MA: Kluwer Academic Publishers.
10. Muggleton, S. (1991). Inductive logic programming. New Generation Computing, 8, 295-318.
11. Sacerdoti, E. D. (1974). Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5, 115-135.
12. Schmid, U. (2001). Inductive synthesis of functional programs. Doctoral dissertation, Technische Universität Berlin, Berlin, Germany.
13. Schoppers, M. J. (1987). Universal plans for reactive robots in unpredictable environments. Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-1987) (pp. 1039-1046). Milan, Italy.
14. Smith, D. R. (1991). KIDS: A knowledge-based software development system. In M. R. Lowry and R. D. McCartney (Eds.), Automating software design. AAAI press.
15. Uther, W. T. B., & Veloso, M. (2000). The lumberjack algorithm for learning linked decision forests. Symposium on Abstraction, Reformulation and Approximation (SARA-2000) (pp. 219-232). Springer Verlag.
16. Veloso, M. M. (1994). Planning and learning by analogical reasoning. Springer Verlag.
17. Winner, E., & Veloso, M. (2002). Analyzing plans with conditional effects. Proceedings of the Sixth International Conference on Artificial Intelligence Planning and Scheduling (AIPS-02) (pp. 271-280). Toulouse, France.