Bandit-based Monte-Carlo structure learning of probabilistic logic programs
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| 008 | 210128e20150701xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-015-5510-3 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-015-5510-3 | ||
| 245 | 0 | 0 | |a Bandit-based Monte-Carlo structure learning of probabilistic logic programs |h [Elektronische Daten] |c [Nicola Di Mauro, Elena Bellodi, Fabrizio Riguzzi] |
| 520 | 3 | |a Probabilistic logic programming can be used to model domains with complex and uncertain relationships among entities. While the problem of learning the parameters of such programs has been considered by various authors, the problem of learning the structure is yet to be explored in depth. In this work we present an approximate search method based on a one-player game approach, called LEMUR. It sees the problem of learning the structure of a probabilistic logic program as a multi-armed bandit problem, relying on the Monte-Carlo tree search UCT algorithm that combines the precision of tree search with the generality of random sampling. LEMUR works by modifying the UCT algorithm in a fashion similar to FUSE, that considers a finite unknown horizon and deals with the problem of having a huge branching factor. The proposed system has been tested on various real-world datasets and has shown good performance with respect to other state of the art statistical relational learning approaches in terms of classification abilities. | |
| 540 | |a The Author(s), 2015 | ||
| 690 | 7 | |a Statistical relational learning |2 nationallicence | |
| 690 | 7 | |a Structure learning |2 nationallicence | |
| 690 | 7 | |a Distribution semantics |2 nationallicence | |
| 690 | 7 | |a Multi-armed bandit problem |2 nationallicence | |
| 690 | 7 | |a Monte Carlo tree search |2 nationallicence | |
| 690 | 7 | |a Logic programs with annotated disjunctions |2 nationallicence | |
| 700 | 1 | |a Di Mauro |D Nicola |u Dipartimento di Informatica, University of Bari "Aldo Moro”, Via Orabona, 4, 70125, Bari, Italy |4 aut | |
| 700 | 1 | |a Bellodi |D Elena |u Dipartimento di Ingegneria, University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy |4 aut | |
| 700 | 1 | |a Riguzzi |D Fabrizio |u Dipartimento di Matematica e Informatica, University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/1(2015-07-01), 127-156 |x 0885-6125 |q 100:1<127 |1 2015 |2 100 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-015-5510-3 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-015-5510-3 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Di Mauro |D Nicola |u Dipartimento di Informatica, University of Bari "Aldo Moro”, Via Orabona, 4, 70125, Bari, Italy |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Bellodi |D Elena |u Dipartimento di Ingegneria, University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Riguzzi |D Fabrizio |u Dipartimento di Matematica e Informatica, University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/1(2015-07-01), 127-156 |x 0885-6125 |q 100:1<127 |1 2015 |2 100 |o 10994 | ||