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Evaluating Tree-Decomposition Based Algorithms for Answer Set Programming

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Learning and Intelligent Optimization (LION 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7219))

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Abstract

A promising approach to tackle intractable problems is given by a combination of decomposition methods with dynamic algorithms. One such decomposition concept is tree decomposition. However, several heuristics for obtaining a tree decomposition exist and, moreover, also the subsequent dynamic algorithm can be laid out differently. In this paper, we provide an experimental evaluation of this combined approach when applied to reasoning problems in propositional answer set programming. More specifically, we analyze the performance of three different heuristics and two different dynamic algorithms, an existing standard version and a recently proposed algorithm based on a more involved data structure, but which provides better theoretical runtime. The results suggest that a suitable combination of the tree decomposition heuristics and the dynamic algorithm has to be chosen carefully. In particular, we observed that the performance of the dynamic algorithm highly depends on certain features (besides treewidth) of the provided tree decomposition. Based on this observation we apply supervised machine learning techniques to automatically select the dynamic algorithm depending on the features of the input tree decomposition.

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Author information

Authors and Affiliations

  1. Institute of Information Systems, Vienna University of Technology, Austria

    Michael Morak, Nysret Musliu, Reinhard Pichler, Stefan Rümmele & Stefan Woltran

Authors
  1. Michael Morak
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  2. Nysret Musliu
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  3. Reinhard Pichler
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  4. Stefan Rümmele
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  5. Stefan Woltran
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Editor information

Editors and Affiliations

  1. Microsoft Research Center, CB3 0FB, Cambridge, UK

    Youssef Hamadi

  2. INRIA Saclay, Université Paris Sud, 91405, Orsay Cedex, France

    Marc Schoenauer

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Morak, M., Musliu, N., Pichler, R., Rümmele, S., Woltran, S. (2012). Evaluating Tree-Decomposition Based Algorithms for Answer Set Programming. In: Hamadi, Y., Schoenauer, M. (eds) Learning and Intelligent Optimization. LION 2012. Lecture Notes in Computer Science, vol 7219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_10

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  • DOI: https://doi.org/10.1007/978-3-642-34413-8_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34412-1

  • Online ISBN: 978-3-642-34413-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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