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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 29 / No.1: The Twenty-Ninth Conference on Artificial Intelligence

Stable Model Counting and Its Application in Probabilistic Logic Programming

March 8, 2023

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Authors

Rehan Aziz

The University of Melbourne


Geoffrey Chu

The University of Melbourne


Christian Muise

The University of Melbourne


Peter Stuckey

The University of Melbourne


DOI:

10.1609/aaai.v29i1.9691


Abstract:

Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving this inference task involves translating the logic program to a propositional theory and using a model counter. In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks. For such problems, direct implementation of stable model semantics allows for more efficient solving. We present two implementation techniques, based on unfounded set detection, that extend a propositional model counter to a stable model counter. Our experiments show that for particular problems, our approach can outperform a state-of-the-art probabilistic logic programming solver by several orders of magnitude in terms of running time and space requirements, and can solve instances of significantly larger sizes on which the current solver runs out of time or memory.

Topics: AAAI

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HOW TO CITE:

Rehan Aziz|| Geoffrey Chu|| Christian Muise|| Peter Stuckey Stable Model Counting and Its Application in Probabilistic Logic Programming Proceedings of the AAAI Conference on Artificial Intelligence, 29 (2015) .

Rehan Aziz|| Geoffrey Chu|| Christian Muise|| Peter Stuckey Stable Model Counting and Its Application in Probabilistic Logic Programming AAAI 2015, .

Rehan Aziz|| Geoffrey Chu|| Christian Muise|| Peter Stuckey (2015). Stable Model Counting and Its Application in Probabilistic Logic Programming. Proceedings of the AAAI Conference on Artificial Intelligence, 29, .

Rehan Aziz|| Geoffrey Chu|| Christian Muise|| Peter Stuckey. Stable Model Counting and Its Application in Probabilistic Logic Programming. Proceedings of the AAAI Conference on Artificial Intelligence, 29 2015 p..

Rehan Aziz|| Geoffrey Chu|| Christian Muise|| Peter Stuckey. 2015. Stable Model Counting and Its Application in Probabilistic Logic Programming. "Proceedings of the AAAI Conference on Artificial Intelligence, 29". .

Rehan Aziz|| Geoffrey Chu|| Christian Muise|| Peter Stuckey. (2015) "Stable Model Counting and Its Application in Probabilistic Logic Programming", Proceedings of the AAAI Conference on Artificial Intelligence, 29, p.

Rehan Aziz|| Geoffrey Chu|| Christian Muise|| Peter Stuckey, "Stable Model Counting and Its Application in Probabilistic Logic Programming", AAAI, p., 2015.

Rehan Aziz|| Geoffrey Chu|| Christian Muise|| Peter Stuckey. "Stable Model Counting and Its Application in Probabilistic Logic Programming". Proceedings of the AAAI Conference on Artificial Intelligence, 29, 2015, p..

Rehan Aziz|| Geoffrey Chu|| Christian Muise|| Peter Stuckey. "Stable Model Counting and Its Application in Probabilistic Logic Programming". Proceedings of the AAAI Conference on Artificial Intelligence, 29, (2015): .

Rehan Aziz|| Geoffrey Chu|| Christian Muise|| Peter Stuckey. Stable Model Counting and Its Application in Probabilistic Logic Programming. AAAI[Internet]. 2015[cited 2023]; .


ISSN: 2374-3468


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