Achieving Artificial General Intelligence Through Peewee Granularity

DOI

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.

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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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 Artificial General Intelligence (2009)
PB  - Atlantis Press
SP  - 198
EP  - 199
SN  - 1951-6851
UR  - https://doi.org/10.2991/agi.2009.42
DO  - 10.2991/agi.2009.42
ID  - Thórisson2009/06
ER  -