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User Modeling pp 365-376 | Cite as

Towards a Bayesian Model for Keyhole Plan Recognition in Large Domains

  • David W. Albrecht
  • Ingrid Zukerman
  • Ann E. Nicholson
  • Ariel Bud
Part of the International Centre for Mechanical Sciences book series (CISM, volume 383)

Abstract

We present an approach to keyhole plan recognition which uses a Dynamic Belief Network to represent features of the domain that are needed to identify users’ plans and goals. The structure of this network was determined from analysis of the domain. The conditional probability distributions are learned during a training phase, which dynamically builds these probabilities from observations of user behaviour. This approach allows the use of incomplete, sparse and noisy data during both training and testing. We present experimental results of the application of our system to a Multi-User Dungeon adventure game with thousands of possible actions and positions. These results show a high degree of predictive accuracy and indicate that this approach will work in other domains with similar features.

Keywords

Bayesian Network Bayesian Model Dynamic Bayesian Network Conditional Probability Distribution Plan Recognition 
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 Wien 1997

Authors and Affiliations

  • David W. Albrecht
    • 1
  • Ingrid Zukerman
    • 1
  • Ann E. Nicholson
    • 1
  • Ariel Bud
    • 1
  1. 1.Department of Computer ScienceMonash UniversityAustralia

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