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Evolutionary Bayesian Network Dynamic Planner for Game RISK

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Applications of Evolutionary Computing (EvoWorkshops 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3005))

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Abstract

Many artificial intelligence problems are susceptible to a goal-directed solution. For some problems, such as dynamic planning and execution, a goal-directed approach may be the only option. Using information available about a desirable state or a measure of acceptability of possible future states, a goal-directed approach determines routes or plans to reach these desirable states. These problems can be categorized as game problems. One such game is RISK. RISK is complex, multi-scaled, and provides a good application for testing a variety of goal-directed approaches. A goal-directed hybrid evolutionary program that plays the RISK game effectively has been developed. This approach advances an understanding of: (1) how to use Bayesian probability to prune combinatorial explosive planning spaces; (2) how to incorporate temporal planning cost in an objective function; and (3) provides a procedure for mapping a problem (i.e., data and knowledge) into a dynamic planning and execution framework.

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Author information

Authors and Affiliations

  1. University of California San Diego 9500 Gilman Dr., La Jolla, CA, 92093, USA

    James Vaccaro & Clark Guest

  2. Lockheed Martin Information Assurance, 4770 Eastgate Mall, San Diego, CA, 92121, USA

    James Vaccaro

Authors
  1. James Vaccaro
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  2. Clark Guest
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Editor information

Editors and Affiliations

  1. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstraße 9–11/1861, 1040, Vienna, Austria

    Günther R. Raidl

  2. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  3. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  4. School of Mathematical and Computer Science, Heriot-Watt University, EH14 8AS, Edinburgh, UK

    David Wolfe Corne

  5. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  6. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

  7. Computing Laboratory, University of Kent, Canterbury, UK

    Colin G. Johnson

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. ICIS, Radboud University Nijmegen, The Netherlands

    Elena Marchiori

  10. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

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© 2004 Springer-Verlag Berlin Heidelberg

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Vaccaro, J., Guest, C. (2004). Evolutionary Bayesian Network Dynamic Planner for Game RISK. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_55

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  • DOI: https://doi.org/10.1007/978-3-540-24653-4_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21378-9

  • Online ISBN: 978-3-540-24653-4

  • eBook Packages: Springer Book Archive

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