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Non-oblivious Strategy Improvement

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Book cover Logic for Programming, Artificial Intelligence, and Reasoning (LPAR 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6355))

Abstract

We study strategy improvement algorithms for mean-payoff and parity games. We describe a structural property of these games, and we show that these structures can affect the behaviour of strategy improvement. We show how awareness of these structures can be used to accelerate strategy improvement algorithms. We call our algorithms non-oblivious because they remember properties of the game that they have discovered in previous iterations. We show that non-oblivious strategy improvement algorithms perform well on examples that are known to be hard for oblivious strategy improvement. Hence, we argue that previous strategy improvement algorithms fail because they ignore the structural properties of the game that they are solving.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Warwick, UK

    John Fearnley

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  1. John Fearnley
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Editor information

Editors and Affiliations

  1. Carnegie Mellon University, USA

    Edmund M. Clarke

  2. School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, M13 9PL, Manchester, UK

    Andrei Voronkov

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Fearnley, J. (2010). Non-oblivious Strategy Improvement. In: Clarke, E.M., Voronkov, A. (eds) Logic for Programming, Artificial Intelligence, and Reasoning. LPAR 2010. Lecture Notes in Computer Science(), vol 6355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17511-4_13

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  • DOI: https://doi.org/10.1007/978-3-642-17511-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17510-7

  • Online ISBN: 978-3-642-17511-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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