A theory of diagnosis from first principles
Artificial Intelligence
Twofold fuzzy sets and rough sets—Some issues in knowledge representation
Fuzzy Sets and Systems
Diagnosis based on subjective information in a solar energy plant
Approximate reasoning in intelligent systems, decision and control
Diagnostics of faulty states in complex physical systems using fuzzy relational equations
Approximate reasoning in intelligent systems, decision and control
Embracing causality in fault reasoning
Artificial Intelligence
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Fuzzy relation equations and causal reasoning
Fuzzy Sets and Systems - Special issue: fuzzy relations, part 2
IPMU '92 Proceedings of the 4th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems: Advanced Methods in Artificial Intelligence
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Relational models for diagnosis are based on a direct description of the association between disorders and manifestations. This type of model has been specially used and developed by Reggia and his co-workers in the late eighties as a basic starting point for approaching diagnosis problems. The paper proposes a new relational model which includes Reggia's model as a particular case and which allows for a more expressive representation of the observations and of the manifestations associated with disorders. The model distinguishes, i) between manifestations which are certainly absent and those which are not (yet) observed, and ii) between manifestations which cannot be caused by a given disorder and manifestations for which we do not know if they can or cannot be caused by this disorder. This new model, which can handle uncertainty in a non-probabilistic way, is based on possibility theory and so-called twofold fuzzy sets, previously introduced by the authors.