Knowledge-Based Decision Model Construction for Hierarchical Diagnosis: A Preliminary Report
This work addresses the lack of research on automating decision model construction in complex belief networks, offering a method for hierarchical diagnosis, though it appears incremental as it builds on existing probabilistic reasoning techniques.
The paper tackles the problem of automating the dynamic, incremental construction of decision models for hierarchical diagnosis, proposing a uniform value-driven method that formulates diagnostic reasoning as a stochastic process using influence diagrams to sequentially determine optimal actions for fault location and repair at minimum cost.
Numerous methods for probabilistic reasoning in large, complex belief or decision networks are currently being developed. There has been little research on automating the dynamic, incremental construction of decision models. A uniform value-driven method of decision model construction is proposed for the hierarchical complete diagnosis. Hierarchical complete diagnostic reasoning is formulated as a stochastic process and modeled using influence diagrams. Given observations, this method creates decision models in order to obtain the best actions sequentially for locating and repairing a fault at minimum cost. This method construct decision models incrementally, interleaving probe actions with model construction and evaluation. The method treats meta-level and baselevel tasks uniformly. That is, the method takes a decision-theoretic look at the control of search in causal pathways and structural hierarchies.