An approach to the compilation of operational knowledge from casual models

An approach to the synthesis and use of operational knowledge in diagnostic problem solving is proposed. The approach significantly departs from previous approaches to knowledge compilation in the sense that the authors do not aim at compiling an autonomous heuristic problem solver from a deep one but only at deriving a set of conditions whose evaluation can focus and speed up diagnostic reasoning on casual models. In particular, it is argued that operational necessary conditions can be used to focus causal diagnostic reasoning by pruning significant portions of the search space to be considered. The process for synthesizing operational knowledge is performed by running a case-independent simulation on a casual model using constraint propagation techniques. This is a major difference with respect to approaches based on the use of examples. Many of the problems arising in the other approaches to the synthesis of heuristics from deep knowledge are solved in this system