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IAT
2008
IEEE

Finding Minimum Data Requirements Using Pseudo-independence

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Finding Minimum Data Requirements Using Pseudo-independence
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 PseudoIndependence (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.
Yoonheui Kim, Victor R. Lesser
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where IAT
Authors Yoonheui Kim, Victor R. Lesser
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