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General Intelligence :
version 2.0
There is nothing so practical as a good theory.
— Kurt Lewin
Everything should be made as simple as possible, but not simpler.
— Albert Einstein
G0 is based on John McCarthy's early idea that intelligence can be captured by declaring what an agent knows (knowledge representation with symbols) while keeping the agent's architecture relatively static; the "declarativist" approach [McCarthy 1958, 1968].
G0 is very similar to Soar (Allen Newell, John Laird, et al) and ACT-R (John Anderson et al) which are among the first unified cognitive architectures, and are based on production rules. There is really nothing new about G0 that has not been done in Soar or ACT-R or as individual research topics. What we try to do here is to integrate all aspects of cognition into a single system, whereas the earlier projects tend to explore these aspects in separate instances.
Recall that in the general intelligence basics section we have the following architecture:
Now we will provide a more detailed architecture showing internal mechanisms of the cognitive engine and memory systems.
This diagram may seem complicated as it has all modules put together:
Common-sense reasoning is a mixture of pattern recognition, logical inference (including deduction and abduction), and natural language processing, hence the blackboard architecture. This seems to be the simplest architecture that meets our requirements.
In the Pattern Recognizer "facts" means sensory events such as "John hits Mary at time t1 ".
"Inductive learner" means that examples drawn from experience are used to induce the formation of new patterns / facts.
For an explanation of specific modules, refer to these pages: [ The contents are still under development, but they will give the readers general guidelines to start thinking about individual sub-problems. ]
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[McCarthy 1958] Programs with Common Sense. Proceedings of the Symposium on Mechanisation of Thought Processes, vol 1 p77-84, London, Her Majesty's Stationery Office.
[McCarthy 1968] Programs with Common Sense. In Minsky (ed), Semantic Information Processing, p403-418. MIT Press, Cambridge, Massachusetts.
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