Continous Conceptual Set Covering: Learning Robot Operators From Examples

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 traytilting 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. Figure 1. Experimental Set Up