期刊
ADVANCES IN INTELLIGENT DATA ANALYSIS XX, IDA 2022
卷 13205, 期 -, 页码 378-391出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-031-01333-1_30
关键词
Learning from interpretations; Probabilistic logic programming; Expectation maximization
类别
资金
- FNRS-FWO joint programme under EOS [30992574]
- Flemish Government
- EU [952215]
- Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation
- KU Leuven Research fund
EMPLiFI is an EM-based parameter learning technique that improves the efficiency of learning by exploiting the rule-based structure of logic programs. It enables parameter learning of multi-head annotated disjunctions in ProbLog programs.
In parameter learning, a partial interpretation most often contains information about only a subset of the parameters in the program. However, standard EM-based algorithms use all interpretations to learn all parameters, which significantly slows down learning. To tackle this issue, we introduce EMPLiFI, an EM-based parameter learning technique for probabilistic logic programs, that improves the efficiency of EM by exploiting the rule-based structure of logic programs. In addition, EMPLiFI enables parameter learning of multi-head annotated dis-junctions in ProbLog programs, which was not yet possible in previous methods. Theoretically, we show that EMPLiFI is correct. Empirically, we compare EMPLiFI to LFI-ProbLog and EMBLEM. The results show that EMPLiFI is the most efficient in learning single-head annotated disjunctions. In learning multi-head annotated disjunctions, EMPLiFI is more accurate than EMBLEM, while LFI-ProbLog cannot handle this task.
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