Name
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 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. |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1016/B978-1-55860-200-7.50125-2 | International Conference on Machine Learning |
Keywords | Field | DocType |
set cover | Nondeterministic algorithm,Computer science,Operator (computer programming),Artificial intelligence,Robot,Machine learning | Conference |
Issue | Citations | PageRank |
1 | 2 | 0.43 |
References | Authors | |
1 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carl Myers Kadie | 1 | 1948 | 196.06 |