Knowledge Growth in an Artificial Animal
This paper describes work using an artificial, behaving, animal model (termed an “ani-mat”) to study intelligence at a primitive level. The motivation for our somewhat unusual approach is the view that the essence of intelligence is exhibited by animals surviving in real environments. Therefore, insight into intelligence should be obtainable from simulated animals and environments, even simple ones, provided the simulations suitably reflect the animal’s survival problems. The starting point for the research is an explicit definition of intelligence which guides model construction. In experiments, a particular animat is placed in an environment and evaluated as to its rates of improvement in performance and perceptual generalization. Learning is central, because we wish to provide the animat with adaptive mechanisms which yield rapid and solid improvement but themselves contain minimal a priori information.
KeywordsAssociation Rule Genetic Operation Total Strength External Reward Sense Vector
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