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Evaluating Inference Algorithms for the Prolog Factor Language

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Inductive Logic Programming (ILP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7842))

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Abstract

Over the last years there has been some interest in models that combine first-order logic and probabilistic graphical models to describe large scale domains, and in efficient ways to perform inference on these domains. Prolog Factor Language (PFL) is a extension of the Prolog language that allows a natural representation of these first-order probabilistic models (either directed or undirected). PFL is also capable of solving probabilistic queries on these models through the implementation of four inference algorithms: variable elimination, belief propagation, lifted variable elimination and lifted belief propagation. We show how these models can be easily represented using PFL and then we perform a comparative study between the different inference algorithms in four artificial problems.

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Authors and Affiliations

  1. CRACS & INESC TEC, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 1021/1055, 4169-007, Porto, Portugal

    Tiago Gomes & Vítor Santos Costa

Authors
  1. Tiago Gomes
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  2. Vítor Santos Costa
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Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy

    Fabrizio Riguzzi

  2. Department of Computer Science and Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Karlovo namesti 13, 12135, Prague 2, Republic Czech

    Filip Železný

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Gomes, T., Santos Costa, V. (2013). Evaluating Inference Algorithms for the Prolog Factor Language. In: Riguzzi, F., Železný, F. (eds) Inductive Logic Programming. ILP 2012. Lecture Notes in Computer Science(), vol 7842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38812-5_6

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  • DOI: https://doi.org/10.1007/978-3-642-38812-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38811-8

  • Online ISBN: 978-3-642-38812-5

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