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Representations for a Complex World: Combining Distributed and Localist Representations for Learning and Planning
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BibTeX
@MISC{Bach_representationsfor,
author = {Joscha Bach},
title = {Representations for a Complex World: Combining Distributed and Localist Representations for Learning and Planning},
year = {}
}
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Abstract
Abstract. To have agents autonomously model a complex environment, it is desirable to use distributed representations that lend themselves to neural learning. Yet developing and executing plans acting on the environment calls for abstract, localist representations of events, objects and categories. To combine these requirements, a formalism that can express neural networks, action sequences and symbolic abstractions with the same means may be considered advantageous. We are currently exploring the use of compositional hierarchies that we treat both as Knowledge Based Artificial Neural Networks and as localist representations for plans and control structures. These hierarchies are implemented using MicroPsi node nets and used in the control of agents situated in a complex simulated environment. 1.
Keyphrases
localist representation combining distributed complex world neural learning complex environment compositional hierarchy symbolic abstraction action sequence neural network micropsi node net complex simulated environment distributed representation control structure artificial neural network
