視聴覚情報の統合化に基づく概念の獲得

抄録

There have been many works to embed the learning capability into computers. But these researches have not yet arrived at the goal. Traditional artificial intelligence systems have been responded to only one kind of external stimulus by given knowledges but has not been able to enhance its ability or efficiency fitted to their environment. In contrast, recent language acquisition systems which efficiently learn the vocabulary and it's meanings by using linguistic and non-linguistic information has been studied. But these systems handle non-linguistic information as predicate calculus instead of the natural stimulus (as visual information) and only small categories of non-linguistic input. From this point of view, we made a concept acquisition system for the purpose of formalizing the method to acquire the concept with two external stimuli, that is, visual scene and auditory sound. Our system acquires concepts without a priori knowledge. This system learns the concepts which contain names, locations, colors and sizes of objects, using visual (image) information and the related auditory (voice) information. The basic operation is to extract a common part or feature from two images or two speech sounds, and is to map the extracted common part of images on the extracted common part of sounds. The correspondence is refined by the generalization and specialization. Consequently, some concepts are acquired about correspondence of voice features to image features, by sequencially learning from image and voice. We have realized the first stage of human's concept acquisition process on a computer system.