Proceedings of the 2nd Conference on Artificial General Intelligence (2009)

Economic Attention Networks: Associative Memory and Resource Allocation for General Intelligence

Authors
Joel Pitt, Matthew Ikle, George Sellmann, Ben Goertzel
Corresponding Author
Matthew Ikle
Available Online June 2009.
DOI
10.2991/agi.2009.19How to use a DOI?
Abstract

A novel method for simultaneously storing memories and allocating resources in AI systems is presented. The method, Economic Attention Networks (ECANs), bears some resemblance to the spread of activation in attractor neural networks, but differs via explicitly differentiating two kinds of "activation" (Short Term Importance, related to processor allocation; and Long Term Importance, related to memory allocation), and in using equations that are based on ideas from economics rather than approximative neural modeling. Here we explain the basic ideas of ECANs, and then investigate the functionality of ECANs as associative memories, via mathematical analysis and the reportage of experimental results obtained from the implementation of ECANs in the OpenCog integrative AGI system.

Copyright
© 2009, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd Conference on Artificial General Intelligence (2009)
Series
Advances in Intelligent Systems Research
Publication Date
June 2009
ISBN
978-90-78677-24-6
ISSN
1951-6851
DOI
10.2991/agi.2009.19How to use a DOI?
Copyright
© 2009, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Joel Pitt
AU  - Matthew Ikle
AU  - George Sellmann
AU  - Ben Goertzel
PY  - 2009/06
DA  - 2009/06
TI  - Economic Attention Networks: Associative Memory and Resource Allocation for General Intelligence
BT  - Proceedings of the 2nd Conference on Artificial General Intelligence (2009)
PB  - Atlantis Press
SP  - 88
EP  - 93
SN  - 1951-6851
UR  - https://doi.org/10.2991/agi.2009.19
DO  - 10.2991/agi.2009.19
ID  - Pitt2009/06
ER  -