Search
1996 Volume 11
Article Contents
RESEARCH ARTICLE   Open Access    

Automatic construction of reactive control systems using symbolic machine learning

More Information
  • 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.
  • 加载中
  • 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 Intelligence64 (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 ControlPeter 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 Research1139–158.

    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 Magazine12 (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

  • Cite this article

    Claude Sammut. 1996. Automatic construction of reactive control systems using symbolic machine learning. The Knowledge Engineering Review. 11: doi: 10.1017/S0269888900007669
    Claude Sammut. 1996. Automatic construction of reactive control systems using symbolic machine learning. The Knowledge Engineering Review. 11: doi: 10.1017/S0269888900007669

Article Metrics

Article views(19) PDF downloads(87)

Other Articles By Authors

RESEARCH ARTICLE   Open Access    

Automatic construction of reactive control systems using symbolic machine learning

The Knowledge Engineering Review  11 Article number: 10.1017/S0269888900007669  (1996)  |  Cite this article

Abstract: 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.

    • Copyright © Cambridge University Press 19961996Cambridge University Press
References (19)
  • About this article
    Cite this article
    Claude Sammut. 1996. Automatic construction of reactive control systems using symbolic machine learning. The Knowledge Engineering Review. 11: doi: 10.1017/S0269888900007669
    Claude Sammut. 1996. Automatic construction of reactive control systems using symbolic machine learning. The Knowledge Engineering Review. 11: doi: 10.1017/S0269888900007669
  • Catalog

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return