Copycat: A computer model of high-level perception and conceptual slippage in analogy making.

Central to every facet of human intelligence are the abilities to flexibly perceive and categorize situations, to see beyond superficial details and understand the essence of a situation, and to make analogies, fluidly translating concepts from one situation into a different situation. This dissertation describes Copycat, a computer model of the mental mechanisms underlying this fluidity of concepts and high-level perception in the context of analogy-making. For the purpose of isolating and modeling the mechanisms underlying these abilities, a microworld has been developed in which analogies can be made between idealized situations involving strings of letters. Analogy-making in this stripped-down, seemingly simple domain requires many of the same abilities humans use to understand and to make analogies between more complex, real-world situations. Copycat constructs interpretations of situations and creates analogies between situations in this microworld. In Copycat, the perception of the essence of a situation and the recognition of essential similarity between two superficially different situations result from the interaction of a large number of simple, independent, and locally-acting perceptual agents with an associative and context-sensitive network of concepts. Central to the model is the notion of statistically emergent high-level behavior, in which the system's low-level activities are permeated with nondeterminism, but more deterministic high-level behavior emerges from the statistics of the low-level nondeterminism. This dissertation first discusses some of the central issues in high-level perception and analogy-making and illustrates how the letter-string microworld captures these issues in an idealized form. A description of the Copycat program is presented, and detailed results of its performance on a number of analogy problems are given, demonstrating the program's flexibility and the range of its abilities. Some problems with the model as it now stands are also discussed. Copycat is then compared with related research in artificial intelligence and cognitive science, and a discussion is given of the program's place in the spectrum of computer models of intelligence, ranging from high-level symbolic models to low-level subsymbolic models.Ph.D.Applied SciencesArtificial intelligenceComputer sciencePsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/128654/2/9116256.pd