Reinforcement learning for symbolic expression induction

We propose a neural network method for the generation of symbolic expressions using reinforcement learning. Usually, the symbolic form expressed in terms of a calculus (propositional, first-order, lambda, etc.) is deemed comprehensible by humans and it is necessary as far as the acceptance of neural networks is concerned.

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Year of publication:	2000
Authors:	Vogiatzis, Dimitrios ; Stafylopatis, Andreas
Published in:	Mathematics and Computers in Simulation (MATCOM). - Elsevier, ISSN 0378-4754. - Vol. 51.2000, 3, p. 169-179
Publisher:	Elsevier
Subject:	Neural networks \| Reinforcement learning \| Symbolic/subsymbolic processing \| Rule extraction

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010749390