Trans-contextual inference system based only on distributed representations

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It is considered that keys to overcome the limitations of classical artificial intelligence are to represent and process distributed information as patterns without symbolizing it, and to apply knowledge learned in a particular context to new situations in various contexts. The present study aimed to develop a pattern-based reasoning system that has high ability of analogical reasoning and common principles to that used in the brain, and obtained the following results.1. Multilayer neural networks, which are typical pattern-based information processing systems, have difficulty in learning input-output relation that depends strongly on context. To overcome this difficulty, we developed the selective desensitization method, with which part of neural elements are desensitized depending on the context. We show by numerical experiments that this model has much higher learning capacity and generalization performance than the conventional ones. This is because two kinds of distributed representations are integrated without using local representations, which greatly expands the potential of neural networks.2. By applying the above method to a kind of neural network called a trajectory attractor model, we built a reasoning system using distributed representations alone. This system deduces a conclusion by state transitions along a trajectory attractor formed in a large-scale dynamical system, and has powerful ability of analogical reasoning. We also showed that this system has many advantages over existing reasoning systems, for example, it can make nonmonotonic reasoning in a simple manner.3. To examine biological plausibility of the principles of our reasoning system, we constructed models of inferotemporal cortex and hippocampus and compare them with neurophysiological data. The results, together with other physiological and psychological grounds, strongly suggested that the selective desensitization method is actually used in the brain.