Abstract
In the paper, we focus on reasoning with IF-THEN rules in propositional fragment of predicate calculus and on its modeling with neural networks. At first, IF-THEN deduction from facts is defined. Then it is proved that for any non-contradictory set of IF-THEN rules and literals (representing facts) there exists a layered recurrent network with 2 hidden layers that can specify all IF-THEN deducible literals. If we denote the set of all literal IF-THEN consequences as D0 and the set of all literal logical consequences as D , then obviously D0 ⊂D . Thus, D0 can be considered to be an approximation of D . Using the designed network for simulation of contradiction proof, the approximation D0 may be easily refined. Furthermore, the network may also be used for determination of D . However, the algorithm that realizes necessary network computations has exponential complexity.
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