Constructing Situation Specific Belief Networks
This work addresses the need for efficient probabilistic reasoning in AI systems, though it appears incremental relative to earlier knowledge-based model construction (KBMC) methods.
The paper tackles the problem of constructing minimal belief networks for specific queries from a knowledge base of network fragments, presenting definitions and conditions that guarantee query completeness.
This paper describes a process for constructing situation-specific belief networks from a knowledge base of network fragments. A situation-specific network is a minimal query complete network constructed from a knowledge base in response to a query for the probability distribution on a set of target variables given evidence and context variables. We present definitions of query completeness and situation-specific networks. We describe conditions on the knowledge base that guarantee query completeness. The relationship of our work to earlier work on KBMC is also discussed.