An alternative computational model for artificial intelligence

Brian Louis Stuart, Purdue University


One objective of artificial intelligence is to mimic the behavior of natural intelligences, and learning is one of the most inherently intelligent activities. Unfortunately, there has not been much success in emulating the low-level learning of classical conditioning and instrumental learning. A novel model of computation, called the cybernetic automaton, which possesses a number of desirable properties, is presented here. First there exists a learning theorem which states that cybernetic automaton learning is in some sense complete. Then through a series of experiments, it is shown that cybernetic automata exhibit many of the properties of classical conditioning and instrumental learning. Finally, the cybernetic automaton model can be implemented using networks of model neurons which are somewhat biologically plausible. Cybernetic automata represent a new and substantial step toward artificially emulating natural behavior.




Lee, Purdue University.

Subject Area

Computer science|Artificial intelligence

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