Continuous Conceptual Set Covering: Learning Robot Operators from Examples

Carl M. Kadie

Microsoft Research, Bldg 9S
Redmond 98052-6399, WA

Author Email: carlk@microsoft.com

Abstract:

Continuous Conceptual Set Covering (CCSC) is an algorithm that uses engineering knowledge to learn operator effects from training examples. The program produces an operator hypothesis that, even in noisy and nondeterministic domains, can make good quantitative predictions. An empirical evaluation in the tray-tilting domain shows that CCSC learns faster than an alternative case-based approach. The best results, however, come from integrating CCSC and the case-based approach.

Proceedings of the Eighth International Conference on Machine Learning, Evanston, Illinois, 1991. (postscript)