Finding Minimum Data Requirements Using Pseudo-independence

Authors:
Yoonheui Kim;Victor Lesser
Affiliations:
-;-
Venue:
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Year:
2008

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Abstract

In situations where Bayesian networks (BN) inferencing approximation is allowable, we show how to reduce the amount of sensory observations necessary and in a multi-agent context the amount of agent communication. To achieve this, we introduce Pseudo-Independence, a relaxed independence relation that quantitatively differentiates the various degrees of independence among nodes in a BN. We combine Pseudo-Independence with Context-Specific Independence to obtain a measure, Context-Specific Pseudo-Independence (CSPI), that determines the amount of required data that needs to be used to infer within the error bound. We then use a Conditional Probability Table-based generation search process that utilize CSPI to determine the minimal observation set. We present empirical results to demonstrate that bounded approximate inference can be made with fewer observations.