Approaches to the study of intelligence

Abstract

How can human and artificial intelligence be understood? This paper reviews Rosenbloom, Laird, Newell, and McCarl's overview of Soar, their powerful symbol-processing simulation of human intelligence. Along the way, the paper addresses some of the general issues to be faced by those who would model human intelligence and suggests that the methods most effective for creating an artificial intelligence might differ from those for modeling human intelligence. Soar is an impressive piece of work, unmatched in scope and power, but it is based in fundamental ways upon Newell's “physical symbol system hypothesis”—any weaknesses in the power or generality of this hypothesis as a fundamental, general characteristic of human intelligence will affect the interpretation of Soar. But our understanding of the mechanisms underlying human intelligence is now undergoing rapid change as new, neurally-inspired computational methods become available that are dramatically different from the symbol-processing approaches that form the basis for Soar. Before we can reach a final conclusion about Soar we need more evidence about the nature of human intelligence. Meanwhile, Soar provides an impressive standard for others to follow. Those who disagree with Soar's assumptions need to develop models based upon alternative hypotheses that match Soar's achievements. Whatever the outcome, Soar represents a major advance in our understanding of intelligent systems.