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A New Stochastic Algorithm for Strategy Optimisation in Bayesian Influence Diagrams

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Book cover Artifical Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6114))

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Abstract

The problem of solving general Bayesian influence diagrams is well known to be NP-complete, whence looking for efficient approximate stochastic techniques yielding suboptimal solutions in reasonable time is well justified. The purpose of this paper is to propose a new stochastic algorithm for strategy optimisation in Bayesian influence diagrams. The underlying idea is an extension of that presented in by Chen who developed a self-annealing algorithm for optimal tour generation in traveling salesman problems (TSP). Our algorithm generates optimal decision strategies by iterative self-annealing reinforced search procedure, gradually acquiring new information while driven by information already acquired. The effectiveness of our method has been tested on computer-generated examples.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruń, Poland

    Michał Matuszak & Tomasz Schreiber

Authors
  1. Michał Matuszak
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  2. Tomasz Schreiber
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley Initiative in Soft Computing (BISC), 94720-1776, CA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

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© 2010 Springer-Verlag Berlin Heidelberg

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Matuszak, M., Schreiber, T. (2010). A New Stochastic Algorithm for Strategy Optimisation in Bayesian Influence Diagrams. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_70

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  • DOI: https://doi.org/10.1007/978-3-642-13232-2_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13231-5

  • Online ISBN: 978-3-642-13232-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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