A neural model for multi-expert architectures

Marc Toussaint

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arXiv · nlin/0202039

A neural model for multi-expert architectures

Marc Toussaint

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Abstract

We present a generalization of conventional artificial neural networks that allows for a functional equivalence to multi-expert systems. The new model provides an architectural freedom going beyond existing multi-expert models and an integrative formalism to compare and combine various techniques of learning. (We consider gradient, EM, reinforcement, and unsupervised learning.) Its uniform representation aims at a simple genetic encoding and evolutionary structure optimization of multi-expert systems. This paper contains a detailed description of the model and learning rules, empirically validates its functionality, and discusses future perspectives.

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nlin.AO cond-mat.dis-nn cs.NE

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BibTeX RIS

Marc Toussaint. 2002-02-19. A neural model for multi-expert architectures. https://arxiv.org/abs/nlin/0202039

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