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Automatic construction of reactive control systems using symbolic machine learning

Published online by Cambridge University Press:  07 July 2009

Claude Sammut
Claude Sammut
Affiliation:
Department of Artificial Intelligence, University of New South Wales, Sydney, NSW 2052, Australia (email: claude@cse. unsw.edu.au)
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Abstract

This paper reviews a number of applications of machine learning to industrial control problems. We take the point of view of trying to automatically build rule-based reactive systems for tasks that, if performed by humans, would require a high degree of skill, yet are generally performed without thinking. Such skills are said to be sub-cognitive. While this might seem restrictive, most human skill is executed subconsciously and only becomes conscious when an unfamiliar circumstance is encountered. This kind of skill lends itself well to representation by a reactive system, that is, one that does not create a detailed model of the world, but rather, attempts to match percepts with actions in a very direct manner.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 11 , Issue 1 , March 1996 , pp. 27 - 42
Copyright
Copyright © Cambridge University Press 1996

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References

Arentz, D, 1994. The Effect of Disturbances in Behavioural Cloning, Computer Engineering Thesis, School of Computer Science and Engineering, University of New South Wales.Google Scholar
Bain, M and Sammut, C, 1995. “A framework for behavioural cloning” In: Furukawa, K, Michie, D and Muggleton, S (eds.), Machine Intelligence 15, Oxford University Press.Google Scholar
Benson, S and Nilsson, NJ, 1995. “Reacting, planning and learning in an autonomous agent” In: Furukawa, K, Michie, D and Muggleton, S (eds.), Machine Intelligence 15, Oxford University Press.Google Scholar
DeJong, G, 1994. “Learning to plan in continuous domains” Artificial Intelligence 64 (1) 71–141.Google Scholar
DeJong, G, 1995. “A case study of explanation-based control” In: Prieditis, A and Russell, S (eds.), International Conference on Machine Learning 167–175, Morgan Kaufmann.Google Scholar
Kerr, RM and Kibira, D, 1994. “Intelligent reactive scheduling by human learning and machine induction” In:IFAC Conference on Intelligent Manufacturing Systems, Vienna.Google Scholar
Leech, WJ, 1986. “A rule based process control method with feedback” In: Proceedings of the ISA/86, Research Triangle Park, NC 27709: The Instrumentation Society of America.Google Scholar
Michie, D, 1986. “The superarticulacy phenomenon in the context of software manufacture” Proc. Royal Society of London A 405 185–212. (Reproduced in D Partridge and Y Wilks (1992) The Foundations of Artificial Intelligence, Cambridge University Press, 411–439.)Google Scholar
Michie, D, Bain, M and Hayes-Michie, JE, 1990. “Cognitive models from subcognitive skills” In: Grimble, M, McGhee, S and Mowforth, P (eds.), Knowledge-base Systems in Industrial Control Peter Peregrinus.Google Scholar
Michie, D and Camacho, R, 1994. “Building symbolic representations of intuitive real-time skill from performance data In: Furakawa, K, Michie, D and Muggleton, S (eds.), Machine Intelligence 13 385–418, The Clarendon Press.Google Scholar
Nilsson, NJ, 1994. “Teleo-Reactive programs for agent control” Journal of Artificial Intelligence Research 1 139–158.Google Scholar
Quinlan, JR, 1993. C4.5: Programs for Machine Learning, Morgan Kaufmann.Google Scholar
Sammut, C and Michie, D, 1991. “Controlling a ‘Black-Box’ simulation of a spacecraft” Al Magazine 12 (1) 56–63.Google Scholar
Sammut, C, Hurst, S, Kedzier, D and Michie, D, 1992. “Learning to fly” In: Sleeman, D and Edwards, P (eds.), Proceedings of the Ninth International Conference on Machine Learning, Morgan Kaufmann.Google Scholar
Schoppers, MJ, 1987. “Universal plans for reactive robots in unpredictable domains” In: Proceedings of IJCAI-87, Morgan Kaufmann.Google Scholar
Stirling, D and Sevinc, S, 1992. “Automated operation of complex machinery using plans extracted from numerical models: Towards adaptive control of a stainless steel cold rolling mill” In: Proceedings of the 5th Australian Joint Conference on Artificial Intelligence.Google Scholar
Stirling, D, 1995. CHURPS: Compressed Heuristic Universal Reaction Planners. PhD Thesis, University of Sydney.Google Scholar
Urbač, T and Bratko, I, 1994. “Reconstructing human skill with machine learning” In: Cohn, A (ed) Proceedings of the 11th European Conference on Artificial Intelligence, John Wiley.Google Scholar
Whitehead, AN, 1911. An Introduction to Mathematics.Google Scholar