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Hypothetical Reasoning in Causal Models
Author(s) -
Console Luca,
Torasso Pietro
Publication year - 1990
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.1990.5.1.83
Subject(s) - formalism (music) , computer science , circumscription , artificial intelligence , model based reasoning , deductive reasoning , logical consequence , theoretical computer science , knowledge representation and reasoning , machine learning , art , musical , visual arts
In this article we propose a logical formalization of the reasoning mechanisms to be adopted for diagnosing multiple faults in a physical (or physiological) system represented by means of causal models. After a brief discussion of the similarities and differences with respect to ontological models, the article presents in detail a formalism to represent causal knowledge and, in particular, an approach to deal with incomplete knowledge. We propose a logical semantic for the formalism and we introduce a precise definition of the concepts of "diagnostic problem" and of "definite and plausible solution to a diagnostic problem." A particular form of hypothetical reasoning is presented in order to deal with incomplete models: the observation (or nonobservation) of findings is used to confirm (reject) the hypothetical assumptions introduced during the diagnostic process. The correspondence between the confirmation criterion we have defined and the circumscription principle is discussed.