Mixture of experts learning in automated theorem proving
| dc.contributor | Graduate Program in Computer Engineering. | |
| dc.contributor.advisor | Güngör, Tunga. | |
| dc.contributor.author | Erkek, Cemal Acar. | |
| dc.date.accessioned | 2023-03-16T10:00:19Z | |
| dc.date.available | 2023-03-16T10:00:19Z | |
| dc.date.issued | 2010. | |
| dc.description.abstract | The main challenge of automated theorem proving is to find a way to shorten the search process. Therefore using a good heuristic method is essential. Although there are several heuristics that improve the search techniques, studies show that a single heuristic cannot cope with all type of problems. The nature of theorem proving problems makes it impossible to find the best universal heuristic, since each problem requires a different search approach. Choosing the right heuristic for a given problem is a difficult task even for an human expert. Machine learning techniques were applied successfully to construct a heuristic in several studies. Instead of constructing a heuristic from scratch, we propose to use the mixture of experts technique to combine the existing heuristics and construct a heuristic. Since each problem requires a different approach, our method uses the output data of a similar problem while learning the heuristic for each new problem. The results show that the combined heuristic is better than each individual heuristic used in combination. | |
| dc.format.extent | 30cm. | |
| dc.format.pages | viii, 32 leaves; | |
| dc.identifier.other | CMPE 2010 E75 | |
| dc.identifier.uri | https://digitalarchive.library.bogazici.edu.tr/handle/123456789/12174 | |
| dc.publisher | Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2010. | |
| dc.subject.lcsh | Neural networks (Computer science) | |
| dc.subject.lcsh | Artificial intelligence. | |
| dc.subject.lcsh | Automatic theorem proving. | |
| dc.title | Mixture of experts learning in automated theorem proving |
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