New PDF release: Approximate Reasoning in Intelligent Systems, Decision and

By E. Sanchez, L. A. Zadeh

ISBN-10: 008034335X

ISBN-13: 9780080343358

Records life like functions of approximate reasoning thoughts, with emphasis put on operational platforms. The papers offered discover new components of sensible decision-making and keep an eye on platforms via contemplating vital elements of fuzzy common sense thought and the most recent advancements within the box of specialist structures. particular fields of software lined contain modelling and keep watch over, administration, making plans, diagnostics, finance and software program. includes 12 papers

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We will show how the ideas from the theory of fuzzy sets as developed by (Zadeh, 1978, 1979) and a theory of evidence as given by (Shafer, 1976) can be combined to form a suitable framework for a logic programming style of reasoning under uncertainty. A restricted form of logic programming in the style of prolog will be used. We could formulate this theory in terms of the first order language of predicate logic but we choose not to do so for computational efficiency considerations. A theorem prover based on the full resolution method with first order predicate logic cannot be made to work fast enough with present techniques of computer implementation.

NOT left of(X,Y) v NOT male(X) v NOT female(Y). (1) (2) (3) (4) (5) (6) (7) The resolution method is then applied to obtain the nil clause. Any two clauses can be clashed if a substitution of the variables can be given which allows an atom of one clause to match the negated atom of the other clause. The result of the clashing is to give a new clause which is the union of the two clashed clauses with the clashed atoms removed and any substitutions used to provide a matching of atoms included. If the nil clause can be obtained then the conclusion does follow logically from the premises, Resolution: NOT male(b) v NOT female(c) NOT male(b) female(b) NOT male(a) v NOT female(b) NOT female(b) NIL using using using using using using (2) (4) (5) (1) (3) (10) and and and and and and (3) (8) (9) (7) (11) (12) (8) (9) (10) (11) (12) so that argument is correct.

Normally the knowledge base would be modified and this should be done in dialogue with the expert who can query the system in order to accept any change. This modification may take the form of chang­ ing an existing fact, rule or combination, or adding further facts of rules to the knowledge base. The disagreement between the computer and the expert may show up a paradox which requires the inference mechanism to be modified and so we also allow for this in the diagram. It should be stressed that present day expert systems do not allow for these types of on line learning mechanisms.

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Approximate Reasoning in Intelligent Systems, Decision and Control by E. Sanchez, L. A. Zadeh

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