Name
Abstract | ||
|---|---|---|
In this paper we present a framework for automated learning within mathematical reasoning systems. In particular, this framework enables proof planning systems to automatically learn new proof methods from well chosen examples of proofs which use a similar reasoning pattern to prove related theorems. Our framework consists of a representation formalism for methods and a machine learning technique which can learn methods using this representation formalism. We present the implementation of this framework within the OmegaMEGA proof planning system, and some experiments we ran on this implementation to evaluate the validity of our approach. |
Year | Venue | Keywords |
|---|---|---|
2002 | FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS | machine learning |
Field | DocType | Volume |
Computer-assisted proof,Computer science,Mathematical proof,Automatic learning,Artificial intelligence,Formalism (philosophy),Proof planning,Machine learning,Mathematical reasoning | Conference | 77 |
ISSN | Citations | PageRank |
0922-6389 | 4 | 0.44 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mateja Jamnik | 1 | 158 | 30.79 |
| Manfred Kerber | 2 | 413 | 60.18 |
| Martin Pollet | 3 | 110 | 13.15 |