Premium
Diagnostic problem‐solving with causal chaining
Author(s) -
Peng Yun,
Reggia James A.
Publication year - 1987
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.4550020303
Subject(s) - chaining , computer science , range (aeronautics) , causal model , artificial intelligence , backward chaining , machine learning , forward chaining , causality (physics) , expert system , mathematics , psychology , inference engine , statistics , materials science , composite material , psychotherapist , physics , quantum mechanics
Parsimonious covering theory is a formal model of abductive diagnostic problem‐solving, Diagnostic knowledge is represented as a network of causal associations, and inferences are made using a sequential hypothesize‐and‐test procedure. In this article, we extend the methods available in parsimonious covering theory and related models to handle causal chaining (situations where multistep causal associations exist between disorders and manifestations). These extensions make parsimonious covering theory capable of representing a much broader range of real‐world diagnostic problems.