Network Fragments: Representing Knowledge for Constructing Probabilistic Models
This work addresses the need for more flexible knowledge representation in probabilistic modeling for complex domains like military situation awareness, though it appears incremental as it builds on existing knowledge-based model construction approaches.
The paper tackles the problem of constructing problem-specific probabilistic models in complex domains by introducing network fragments as larger, semantically meaningful units of knowledge, enabling representation of asymmetric independence and canonical intercausal interaction, with examples from military situation awareness.
In most current applications of belief networks, domain knowledge is represented by a single belief network that applies to all problem instances in the domain. In more complex domains, problem-specific models must be constructed from a knowledge base encoding probabilistic relationships in the domain. Most work in knowledge-based model construction takes the rule as the basic unit of knowledge. We present a knowledge representation framework that permits the knowledge base designer to specify knowledge in larger semantically meaningful units which we call network fragments. Our framework provides for representation of asymmetric independence and canonical intercausal interaction. We discuss the combination of network fragments to form problem-specific models to reason about particular problem instances. The framework is illustrated using examples from the domain of military situation awareness.