Speeding up parameter and rule learning for acyclic probabilistic logic programs

Faria, Francisco Henrique Otte Vieira de; Gusmão, Arthur Colombini; Bona, Glauber De; Mauá, Denis Deratani; Cozman, Fábio Gagliardi

doi:10.1016/j.ijar.2018.12.012

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Q4 Does EMPLiFI require more computational resources? Emergency Power Supply (EPS) [18] is propositional, acyclic and contains 24 probabilities 5 . It can be handled by all learner as it has no multi-head ADs.…”

Section: Methodsmentioning

confidence: 99%

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Parameter Learning in ProbLog with Annotated Disjunctions

Yang

Jain

Raedt

et al. 2022

Lecture Notes in Computer Science

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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, EM-PLiFI enables parameter learning of multi-head annotated disjunctions 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 in learning single-head annotated disjunctions, EMPLiFI is the most efficient. In learning multi-head annotated disjunctions, EMPLiFI is more accurate than EMBLEM, while LFI-ProbLog cannot handle this task.

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Section: Methodsmentioning

confidence: 99%

“…In extreme cases, it can converge to incorrect values. To tackle this issue, Asteroidea [6,18] avoids EM iterations for probabilistic rules, which is a specialization of EMPLiFI that supports single-head ADs, but not multi-head ADs. EMBLEM [1] is another EM-based parameter learner.…”

Section: Related Workmentioning

confidence: 99%