Conformant Planning as a Case Study of Incremental QBF Solving

  • Uwe Egly
  • Martin Kronegger
  • Florian Lonsing
  • Andreas Pfandler
Conference paper

DOI: 10.1007/978-3-319-13770-4_11

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8884)
Cite this paper as:
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

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.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Uwe Egly
    • 1
  • Martin Kronegger
    • 1
  • Florian Lonsing
    • 1
  • Andreas Pfandler
    • 1
    • 2
  1. 1.Institute of Information SystemsVienna University of TechnologyAustria
  2. 2.School of Economic DisciplinesUniversity of SiegenGermany

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