Interval-type and affine arithmetic-type techniques for handling uncertainty in expert systems |
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| Authors: | Martine Ceberio,Vladik Kreinovich,Sanjeev Chopra,Luc Longpré ,Hung T. Nguyen,Bertram Ludä scher,Chitta Baral |
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| Affiliation: | 1. NASA Pan-American Center for Earth and Environmental Studies, University of Texas, El Paso, TX 79968, USA;2. Mathematics Department, New Mexico State University, Las Cruces, NM 88003, USA;3. Department of Computer Science, University of California, Davis, CA 95616, USA;4. Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287-5406, USA |
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| Abstract: | Expert knowledge consists of statements Sj (facts and rules). The facts and rules are often only true with some probability. For example, if we are interested in oil, we should look at seismic data. If in 90% of the cases, the seismic data were indeed helpful in locating oil, then we can say that if we are interested in oil, then with probability 90% it is helpful to look at the seismic data. In more formal terms, we can say that the implication “if oil then seismic” holds with probability 90%. Another example: a bank A trusts a client B, so if we trust the bank A, we should trust B too; if statistically this trust was justified in 99% of the cases, we can conclude that the corresponding implication holds with probability 99%. |
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| Keywords: | Interval computations Affine arithmetic Expert systems Fuzzy logic Geoinformatics Computer security |
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