Lifted discriminative learning of probabilistic logic programs

Probabilistic logic programming (PLP) provides a powerful tool for reasoning with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. I...

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Bibliographic Details
Published in:Machine learning Vol. 108; no. 7; pp. 1111 - 1135
Main Authors: Nguembang Fadja, Arnaud, Riguzzi, Fabrizio
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2019
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Control
Inference
Logic programming
Logic programs
Machine learning
Mathematical models
Mechatronics
Natural Language Processing (NLP)
Parameters
Robotics
Simulation and Modeling
Software
Special Issue of the Inductive Logic Programming (ILP)
Expectation maximization
Lifted inference
Probabilistic inductive logic programming
Statistical relational learning
Probabilistic logic programming
ISSN:0885-6125, 1573-0565
Online Access:Get full text
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Summary:Probabilistic logic programming (PLP) provides a powerful tool for reasoning with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. In this paper we consider PLP models that are amenable to lifted inference and present an algorithm for performing parameter and structure learning of these models from positive and negative examples. We discuss parameter learning with EM and LBFGS and structure learning with LIFTCOVER, an algorithm similar to SLIPCOVER. The results of the comparison of LIFTCOVER with SLIPCOVER on 12 datasets show that it can achieve solutions of similar or better quality in a fraction of the time.
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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-018-5750-0