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Temporal Relevance in Dynamic Decision Networks with Sparse Evidence

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Abstract

Dynamic decision networks have been used in many applications and they are particularly suited for monitoring applications. However, the networks tend to grow very large resulting in significant performance degradation. In this paper, we study the degeneration of relevance of uncertain temporal information and propose an analytical upper bound for the relevance time of information in a restricted class of dynamic decision networks with sparse evidence. An empirical generalization of this analytical result is presented along with a series of experimental results to verify the performance of the empirical upper bound. By discarding irrelevant and weakly relevant evidence, the performance of the network is significantly improved.

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Authors and Affiliations

  1. School of Computer Science, University of Windsor, Windsor, Ontario, Canada

    Ahmed Y. Tawfik

  2. Department of Computer Science, York University, Toronto, Ontario, Canada

    Shakil Khan

Authors
  1. Ahmed Y. Tawfik
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  2. Shakil Khan
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Correspondence to Ahmed Y. Tawfik.

Additional information

Work supported in part through a Discovery Grant from the Natural Sciences and Engineering Research Council, Canada.

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Tawfik, A.Y., Khan, S. Temporal Relevance in Dynamic Decision Networks with Sparse Evidence. Appl Intell 23, 87–96 (2005). https://doi.org/10.1007/s10489-005-3414-9

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  • DOI: https://doi.org/10.1007/s10489-005-3414-9

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