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Logical inference and inductive learning in artificial neural networks

Logical inference and inductive learning in artificial neural networks,S. D Artur,Gerson Zaverucha,Luis A. V. De Carvalho

Logical inference and inductive learning in artificial neural networks   (Citations: 12)
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Published in 1997.
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    • ...As [26] employed binary threshold units as activation functions of the network nodes, the results were lifted to sigmoidal and hence differentiable activation functions in [20, 19]...

    Jens Lehmannet al. Extracting reduced logic programs from artificial neural networks

    • ...A combined approach was finally presented in [4], allowing an intuitive oneto-one transformation of propositional logic to neural nets while maintaining learning capabilities...

    Daniel Michulkeet al. Neural Networks for State Evaluation in General Game Playing

    • ...This problem was solved in [13] by showing that the same results can be achieved if bipolar sigmoidal units are used instead...
    • ...[13] also overcomes a restriction of the KBANN method originally presented in [35]: rules may now have arbitrarily many preconditions and programs may have arbitrarily many rules with the same postcondition...
    • ...While it was shown in [13], that the propositional core method leads to improved training behaviour, it is important to notice that propositional logic is in general insucient for knowledge based intelligent systems...

    Sebastian Baderet al. Connectionist Model Generation: A First-Order Approach

    • ...This problem was solved in [11] by showing that the same results can be achieved if bipolar sigmoidal units are used instead (see also [8])...
    • ...[11] also overcomes a restriction of the KBANN method originally presented in [32]: rules may now have arbitrarily many preconditions and programs may have arbitrarily many rules with the same postcondition...

    Sebastian Baderandet al. The Core Method: Connectionist Model Generation

    • ...There were several attempts to make these neural networks more “intelligent”, see, for example, [5], [6], [7] for some further developments...

    Ekaterina Komendantskaya. Learning and deduction in neural networks and logic

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