Quantitative Symbolic Process Models: How a Fair Fraction of Intelligence Could Be Abstracted in AI Research

Mjolsness, Eric (University of California, Irvine)

AAAI Conferences 

Quantitative symbolic process models are proposed as a high-level model representation for probabilistic causal knowledge and know-how. These representations have suitable expressive power and mathematical foundation to serve as an abstraction of intelligence in much of AI research.

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