Integrating induction and abduction in logic programming
We propose an approach for the integration of abduction and induction in Logic Programming. We define an Abductive Learning Problem as an extended Inductive Logic Programming problem where both the background and target theories are abductive theories and where abductive derivability is used as the...
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| Published in: | Information sciences Vol. 116; no. 1; pp. 25 - 54 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.05.1999
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | We propose an approach for the integration of abduction and induction in Logic Programming. We define an Abductive Learning Problem as an extended Inductive Logic Programming problem where both the background and target theories are abductive theories and where abductive derivability is used as the coverage relation instead of deductive derivability. The two main benefits of this integration are the possibility of learning in presence of incomplete knowledge and the increased expressive power of the background and target theories. We present the system LAP (Learning Abductive Programs) that is able to solve this extended learning problem and we describe, by means of examples, four different learning tasks that can be performed by the system: learning from incomplete knowledge, learning rules with exceptions, learning from integrity constraints and learning recursive predicates. |
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| AbstractList | We propose an approach for the integration of abduction and induction in Logic Programming. We define an Abductive Learning Problem as an extended Inductive Logic Programming problem where both the background and target theories are abductive theories and where abductive derivability is used as the coverage relation instead of deductive derivability. The two main benefits of this integration are the possibility of learning in presence of incomplete knowledge and the increased expressive power of the background and target theories. We present the system LAP (Learning Abductive Programs) that is able to solve this extended learning problem and we describe, by means of examples, four different learning tasks that can be performed by the system: learning from incomplete knowledge, learning rules with exceptions, learning from integrity constraints and learning recursive predicates. |
| Author | Mello, Paola Milano, Michela Riguzzi, Fabrizio Lamma, Evelina |
| Author_xml | – sequence: 1 givenname: Evelina surname: Lamma fullname: Lamma, Evelina – sequence: 2 givenname: Paola surname: Mello fullname: Mello, Paola – sequence: 3 givenname: Michela surname: Milano fullname: Milano, Michela – sequence: 4 givenname: Fabrizio surname: Riguzzi fullname: Riguzzi, Fabrizio email: friguzzi@deis.unibo.it |
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| Cites_doi | 10.1016/0004-3702(92)90075-9 10.1007/3-540-60454-5_30 10.1111/j.1467-8640.1994.tb00167.x 10.1007/BF03037227 10.1007/BF00992863 10.1016/S0304-3975(97)80755-4 10.1093/logcom/2.6.719 10.1007/BF03037228 |
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| Keywords | Inductive Logic Programming Abductive Logic Programming Nonmonotonic reasoning Machine learning |
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| SubjectTerms | Abductive Logic Programming Inductive Logic Programming Machine learning Nonmonotonic reasoning |
| Title | Integrating induction and abduction in logic programming |
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