Achieving Artificial General Intelligence Through Peewee Granularity
Kristinn R. Thórisson, Eric Nivel
Kristinn R. Thórisson
Available Online June 2009.
- https://doi.org/10.2991/agi.2009.42How to use a DOI?
- 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.
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 -