A Theory of Diagnosis for Incomplete Causal Models

One of the problems of the recent approaches to problem solving based on deep knowledge is the lack of a formal treatment of incomplete knowledge. However, dealing with incomplete models is fundamental to many realworld domains. In this paper we propose a formal theory of causal diagnostic reasoning, dealing with different forms of incompleteness both in the general causal knowledge (missing or abstracted knowledge) and in the data describing a specific case under examination. Different forms of nonmonotonic reasoning (hypothetical and circumscriptive reasoning) are used in order to draw and confirm conclusions from incomplete knowledge. Multiple fault solutions are treated in a natural way and parsimony criteria arc used to rank alternative solutions.