Abstract
Practising to operate an unknown system and observing the input and output of the system, in a sense, helps to optimally control that system. The acquired knowledge, is, in turn, used to solve future analogous control problems. This means that it is very important to know how to memorize the acquired knowledge and to utilize it for learning. In this paper, we propose a new knowledge representation and reasoning method and develop a learning machine (KBLC: Knowledge-Based Learning Controller) by using them. A simple implementation has been constructed that demonstrates the feasibility of building such a machine.
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Suganuma, Y., Ito, M. Learning control and knowledge representation. J Intell Robot Syst 2, 337–358 (1989). https://doi.org/10.1007/BF00238696
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DOI: https://doi.org/10.1007/BF00238696