Name
Abstract | ||
|---|---|---|
This paper discusses a knowledge-base encoding methodology for diagnostic tasks, transforming such knowledge into constrained optimization problems. The methodology is based on a reinterpretation of the consistent causal reasoning paradigm [D. Dubois, H. Prade, Fuzzy relation equations and causal reasoning, Fuzzy Sets and Systems 45 (2) (1995) 119-134] as an equivalent problem of feasibility subject to equality and inequality constraints (in the binary case). Then, it is extended to the fuzzy case. Preferences under uncertain knowledge are incorporated by transforming the feasibility problem into an optimization one, which may be interpreted in possibilistic terms. The problem is solved by efficient, widely-known, linear and quadratic programming tools, which are able to cope with large-scale problems. Examples illustrating some of the concepts and possibilities of the proposed procedure, as well as a summary comparison with other approaches are also discussed. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.ins.2008.07.017 | Inf. Sci. |
Keywords | Field | DocType |
consistent causal reasoning paradigm,equivalent problem,fuzzy relation equation,feasibility subject,possibilistic reasoning,binary case,fuzzy case,feasibility problem,causal reasoning,fuzzy possibilistic diagnostics,large-scale problem,optimization problem,fault detection and diagnosis,a pproximate reasoning,optimisation,optimization,knowledge base,quadratic program | Causal reasoning,Mathematical optimization,Fuzzy logic,Fuzzy set,Artificial intelligence,Constrained optimization problem,Quadratic programming,Optimization problem,Mathematics,Machine learning,Binary number,Encoding (memory) | Journal |
Volume | Issue | ISSN |
178 | 22 | 0020-0255 |
Citations | PageRank | References |
7 | 0.53 | 24 |
Authors | ||
1 | ||