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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
FuK., Learning control systems — review and outlook, IEEE Trans. Automatic Control, AC-15, 210–221 (1970).
NarendraS. and ThathacharM.A.L., Learning automata—a survey, IEEE Trans. Systems, Man, and Cybernetics, SMC-4, 323–334 (1974).
SklanskiJ., Learning systems for automatic control, IEEE Trans. Automatic Control AC-11, 6–19 (1966).
BartoA.G., SuttonR.S., and AndersonC.W., Neuronlike elements that can solve difficult learning control problems, IEEE Trans. Systems, Man, and Cybernetics SMC-13, 834–846, (1983).
Ersü, E., and Tolle, H., A new concept for learning control inspired by brain theory, Internat. Federation of Automatic Control 9th World Congress: A bridge between Control Science and Technology (1985) pp. 245–250.
Goldberg, D.E., Dynamic system control using rule learning and genetic algorithms, Proc. IJCAI-85, Los Angeles (1985) pp. 588–592.
Selfridge, O.G., Sutton, R.S., and Barto, A.G., Training and tracking in robotics, Proc. IJACI-85, Los Angeles (1985) pp. 670–672.
KawamuraK., MiyazakiF., and ArimotoS., Proposal of betterment process: a learning control method for dynamic systems, Trans. Soc. Instrument and Control Engineers 22, 56–62 (1986) (in Japanese).
ShinS., and KitamoriT., Model reference learning control for discrete-time linear time-varying systems, Trans. Soc. Instrument and Control Engineers 22, 19–24 (1986) (in Japanese).
Yamazaki T., and Sugeno M., Self-organizing fuzzy controller, Trans. Soc. Instrument and Control Engineers 20 (1984) (in Japanese).
CohenP.R., and FeigenbaumE.A., (eds.), The Handbook of Artificial Intelligence, Vol. III, William Kaufmann, Los Altos (1982).
AnzaiY., Cognitive control of real-time event-drive systems, Cognitive Sci., 8, 221–254 (1984).
KnaeuperA., and RouseW.B., A rule-based model of human problem-solving behavior in dynamic environments, IEEE Trans. Systems, Man, and Cybernetics SMC-15, 708–719 (1985).
BroadbentD.E., Levels, hierarchies, and the locus of control, Quart. J. Experimental Psychology 29, 181–201 (1977).
GetnerD., Structure-mapping: a, theoretical framework for analogy, Cognitive Sci. 7, 155–170 (1983).
TverskyA., Features of similarity, Psychological Rev. 84, 327–352 (1977).
WinstonP.H., Learning and reasoning by analogy, Commun. ACM 23, 689–703 (1980).
WinstonP.H., Learning new principles from precedents and exercises, Artificial Intelligence 19, 321–350 (1982).
UtgoffP.E., Shift of bias for inductive concept learning, in Machine Learning: An Artificial Intelligence Approach, Vol. II (eds. R. S.Michalsky, J.G.Carbonell and T.M.Mitchell) Morgan Kaufmann, Los Angeles (1986).
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Suganuma, Y., Ito, M. Learning control and knowledge representation. J Intell Robot Syst 2, 337–358 (1989). https://doi.org/10.1007/BF00238696
Received:
Issue Date:
DOI: https://doi.org/10.1007/BF00238696