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Knowledge acquisition for goal prediction in a multi-user adventure game

  • D. W. Albrecht
  • A. E. Nicholson
  • I. Zukerman
Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1394)

Abstract

We present an approach to goal recognition which uses a Dynamic Belief Network to represent domain features needed to identify users' goals and plans. Different network structures have been developed, and their conditional probability distributions have been automatically acquired from training data. These networks show a high degree of accuracy in predicting users' goals. Our approach allows the use of incomplete, sparse and noisy data during both training and testing. We then apply simple learning techniques to learn significant actions in the domain. This speeds up the performance of the most promising dynamic belief networks without loss in predictive accuracy.

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References

  1. Albrecht, D. W., Zukerman, I., and Nicholson, A. E. (1997). Bayesian models for keyhole plan recognition in an adventure game (extended version). Technical Report 328, Department of Computer Science, Monash University, Victoria, Australia.Google Scholar
  2. Good, I. J. (1965). The Estimation of Probabilities: An Essay on Modern Bayesian Methods. Research Monograph No. 30. Cambridge, Massachusetts: MIT Press.MATHGoogle Scholar
  3. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. San Mateo, California: Morgan Kaufmann Publishers.MATHGoogle Scholar
  4. Wallace, C. (1990). Classification by minimum-message-length inference. In Goos, G., and Hartmanis, J., eds., ICCI '90-Advances in Computing and Information. Berlin: Springer-Verlag. 72–81.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • D. W. Albrecht
    • 1
  • A. E. Nicholson
    • 1
  • I. Zukerman
    • 1
  1. 1.Department of Computer ScienceMonash UniversityClaytonAustralia

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