Abstract
We have seen that new techniques allow to design automata networks, capable of learning how to solve problems of high order. Those techniques clearly go beyond the limitations of the perceptron and are very different, in their spirit, from the techniques usually used in Artificial Intelligence.
They are based on a representation of knowledge which is distributed, fault tolerant, and an inference mechanism which is dynamical. These aspects make this approach look more like the way the brain works than the usual Al approach.
In the case of the learning-from-examples problem, it is possible to envision these methods as capable of automatically generate predicates characteristic of the set of examples, which would allow to use them as first steps in more classical Al systems (Expert systems for example).
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Soulie, F.F. (1988). Representation of knowledge and learning on automata networks. In: Choffrut, C. (eds) Automata Networks. LITP 1986. Lecture Notes in Computer Science, vol 316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-19444-4_17
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