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The frame problem and Bayesian network action representations

  • Craig Boutilier
  • Moisés Goldszmidt
Knowledge Representation II: Actions
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1081)

Abstract

We examine a number of techniques for representing actions with stochastic effects using Bayesian networks and influence diagrams. We compare these techniques according to ease of specification and size of the representation required for the complete specification of the dynamics of a particular system, paying particular attention the role of persistence relationships. We precisely characterize two components of the frame problem for Bayes nets and stochastic actions, propose several ways to deal with these problems, and compare our solutions with Reiter's solution to the frame problem for the situation calculus. The result is a set of techniques that permit both ease of specification and compact representation of probabilistic system dynamics that is of comparable size (and timbre) to Reiter's representation (i.e., with no explicit frame axioms).

Keywords

Bayesian Network Compact Representation Frame Problem Conditional Probability Table Influence Diagram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Craig Boutilier
    • 1
  • Moisés Goldszmidt
    • 2
  1. 1.Dept. of Computer ScienceUniversity of British ColumbiaVancouverCanada
  2. 2.Rockwell Science CenterPalo AltoUSA

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