Name
Abstract | ||
|---|---|---|
A neural approach to propositional multi-adjoint logic programming was recently introduced. In this paper we extend the neural approach to multiadjoint deduction and, furthermore, modify it to cope with abductive multiadjoint reasoning, where adaptations of the uncertainty factor in a knowledge base are carried out automatically so that a number of given observations can be adequately explained. |
Year | Venue | Keywords |
|---|---|---|
2002 | AIMSA | multi-adjoint logic programming,abductive multiadjoint reasoning,neural approach,uncertainty factor,knowledge base,abductive multi-adjoint reasoning,abductive reasoning,medical diagnosis,many valued logic,neural nets,classical logic,fuzzy set theory,data analysis,neural network,human cognition,logic programming,knowledge representation,neural net,probability theory |
Field | DocType | Volume |
Computer science,Propositional calculus,Abductive logic programming,Artificial intelligence,Non-monotonic logic,Abductive reasoning,Deductive reasoning,Knowledge base,Logic programming,Artificial neural network,Machine learning | Conference | 2443 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-44127-1 | 3 |
PageRank | References | Authors |
0.48 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesús Medina | 1 | 933 | 70.56 |
| Enrique Mérida Casermeiro | 2 | 22 | 5.38 |
| Manuel Ojeda-aciego | 3 | 748 | 68.13 |