Vector Symbolic Architectures: A New Building Material for Artificial General Intelligence

Authors:
Simon D. Levy;Ross Gayler
Affiliations:
Washington and Lee University, USA;Veda Advantage Solutions, Australia
Venue:
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Year:
2008

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Abstract

We provide an overview of Vector Symbolic Architectures (VSA), a class of structured associative memory models that offers a number of desirable features for artificial general intelligence. By directly encoding structure using familiar, computationally efficient algorithms, VSA bypasses many of the problems that have consumed unnecessary effort and attention in previous connectionist work. Example applications from opposite ends of the AI spectrum --visual map-seeking circuits and structured analogy processing --attest to the generality and power of the VSA approach in building new solutions for AI.