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Conformant Planning as a Case Study of Incremental QBF Solving

  • Conference paper
Artificial Intelligence and Symbolic Computation (AISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8884))

Abstract

We consider planning with uncertainty in the initial state as a case study of incremental quantified Boolean formula (QBF) solving. We report on experiments with a workflow to incrementally encode a planning instance into a sequence of QBFs. To solve this sequence of successively constructed QBFs, we use our general-purpose incremental QBF solver DepQBF. Since the generated QBFs have many clauses and variables in common, our approach avoids redundancy both in the encoding phase and in the solving phase. Experimental results show that incremental QBF solving outperforms non-incremental QBF solving. Our results are the first empirical study of incremental QBF solving in the context of planning and motivate its use in other application domains.

Supported by the Austrian Science Fund (FWF) under grants S11409-N23 and P25518-N23 and the German Research Foundation (DFG) under grant ER 738/2-1.

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Author information

Authors and Affiliations

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

    Uwe Egly, Martin Kronegger, Florian Lonsing & Andreas Pfandler

  2. School of Economic Disciplines, University of Siegen, Germany

    Andreas Pfandler

Authors
  1. Uwe Egly
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  2. Martin Kronegger
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  3. Florian Lonsing
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  4. Andreas Pfandler
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Editor information

Editors and Affiliations

  1. Department of Information Technologies, University of Huelva, Crta. Palos de la Frontera, s/n, 21810, Palos de la Frontera, Spain

    Gonzalo A. Aranda-Corral

  2. Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131, Karlsruhe, Germany

    Jacques Calmet

  3. Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Sevilla, Avda. Reina Mercedes, s/n, 41012, Sevilla, Spain

    Francisco J. Martín-Mateos

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Egly, U., Kronegger, M., Lonsing, F., Pfandler, A. (2014). Conformant Planning as a Case Study of Incremental QBF Solving. In: Aranda-Corral, G.A., Calmet, J., Martín-Mateos, F.J. (eds) Artificial Intelligence and Symbolic Computation. AISC 2014. Lecture Notes in Computer Science(), vol 8884. Springer, Cham. https://doi.org/10.1007/978-3-319-13770-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-13770-4_11

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13769-8

  • Online ISBN: 978-3-319-13770-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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