Temporal Relevance in Dynamic Decision Networks with Sparse Evidence

Authors:
Ahmed Y. Tawfik;Shakil Khan
Affiliations:
School of Computer Science, University of Windsor, Windsor, Canada;Department of Computer Science, York University, Toronto, Canada
Venue:
Applied Intelligence
Year:
2005

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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.