Reinforcement learning for symbolic expression induction

Dimitrios Vogiatzis and Andreas Stafylopatis

Mathematics and Computers in Simulation (MATCOM), 2000, vol. 51, issue 3, 169-179

Abstract: 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.

Keywords: Neural networks; Reinforcement learning; Symbolic/subsymbolic processing; Rule extraction (search for similar items in EconPapers)
Date: 2000
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:51:y:2000:i:3:p:169-179

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