Abstract
BOXES is a well known methodology that learns to perform control maneuvers for dynamic systems with only cursory a priori knowledge of the mathematics of the system model. A limiting factor in the BOXES algorithm has always been the assignment of appropriate boundaries to subdivide each state variable into regions. In addition to suggesting a method of alleviating this weakness, the paper shows that the accumulated statistical data in near neighboring states may be a powerful agent in accelerating learning, and may eventually provide a possible evolution to self-organization. A heuristic process is postulated that may allow strong areas of knowledge in the system domain to create larger cellular structures, while causing the more delicate, uncertain, or untested regions to take on a finer granularity. The paper theorizes that these untrainable states may contain valuable and sufficient information about the location of switching surfaces. The paper concludes with some future research questions.
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References
Blum, B.I. 1996. Beyond Programming: to a New Era of Design, 132–134. New York: Oxford University Press
Canudas de Wit, C. A. 1988. Adaptive Control for Partially-known Systems, 99ff. Amsterdam: Elsevier
Michie, D. and Chambers, R.A. 1968. BOXES—an Experiment in Adaptive Control. Machine Intelligence II, 137–152. London: Oliver & Boyd
Russell, D.W., Rees S.J. and Boyes, J.A. 1977. A Micro-system for Control by Automata in Real-Life Situations. Proc. Conf. on Information Sciences and Systems. Johns Hopkins University, Baltimore. 226–230
Widjaja M. and Yurkovich, S. 1995 Intelligent Control for Swing Up and Balancing of an Inverted Pendulum System. Proc. 4 th. IEEE Conference on Control Applications, Albany New York. 534–542.
Russell, D.W. and Rees, S.J. 1975. System Control—a Case Study of a Statistical Learning Automaton. Progress in Cybernetics Systems Research 2:114–120, New York: Hemisphere Publishing Co.
Rees, S.J. PhD Thesis. Liverpool Polytechnic, 1977
Russell, D. W., 1993. A Critical Assessment of the BOXES Paradigm. J. Applied Artificial Intelligence 7(4): 383–395
Russell, D. W., 1991. Further Studies in AI-Augmented Process Control using the BOXES Methodology. Proc. 3rd. IFAC Int. Workshop on Artificial Intelligence in Real-Time Systems:AIRTC’91. September 23–25, Sonoma, CA. Pergammon Press
Russell, D.W., 1995. Advisor Logic. Control Engineering Practice 3(7): 977–984 July, Oxford: Pergammon Press.
Humphrey, N. 1992. A History of the Mind, 82–91. New York: Simon & Schuster
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Russell, D.W. (2002). A Proposed Evolutionary, Self-Organizing Automaton for the Control of Dynamic Systems. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_5
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DOI: https://doi.org/10.1007/3-540-45813-1_5
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