Achieving Artificial General Intelligence Through Peewee Granularity

Authors
Kristinn R. Thórisson, Eric Nivel
Corresponding Author
Kristinn R. Thórisson
Available Online June 2009.
DOI
https://doi.org/10.2991/agi.2009.42How to use a DOI?
Abstract
The general intelligence of any autonomous system must in large part be measured by its ability to automatically learn new skills and integrate these with prior skills. Cognitive architectures addressing these topics are few and far between ­ possibly because of their difficulty. We argue that architectures capable of diverse skill acquisition and integration, and real-time management of these, require an approach of modularization that goes well beyond the current practices, leading to a class of architectures we refer to as peewee-granule systems. The building blocks (modules) in such systems have simple operational semantics and result in architectures that are heterogeneous at the cognitive level but homogeneous at the computational level.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
Proceedings of the 2nd Conference on Artificiel General Intelligence (2009)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2009
ISBN
978-90-78677-24-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/agi.2009.42How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Kristinn R. Thórisson
AU  - Eric Nivel
PY  - 2009/06
DA  - 2009/06
TI  - Achieving Artificial General Intelligence Through Peewee Granularity
BT  - Proceedings of the 2nd Conference on Artificiel General Intelligence (2009)
PB  - Atlantis Press
SP  - 198
EP  - 199
SN  - 1951-6851
UR  - https://doi.org/10.2991/agi.2009.42
DO  - https://doi.org/10.2991/agi.2009.42
ID  - Thórisson2009/06
ER  -