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.

• Type: Conference
• Year: 2008
• Where: IAT
• Authors: Yoonheui Kim, Victor R. Lesser
• Keywords: IAT 2008, Intelligent Agent, Probability Table-based Generation, Relaxed Independence Relation, Sensory Observations