Abstract
A framework for knowledge-based control is proposed. The approach presented is suitable for control systems and control support of systems which have no adequate mathematical models. Thus, the control is performed by using knowledge engineering methods rather than pure mathematical control methods. The domain expert's knowledge is assumed to be encoded in the form of simple statements (facts) and special reasoning rules, which form the core of the Knowledge-Based Control System (KBCS). The control system reads the input information, and on the basis of the current state of its knowledge base, together with the application of supplied inference rules updates the knowledge base and performs the required control actions. Moreover, some inference control knowledge, reflecting the expert's way of reasoning, is to be incorporated in the KBCS. The main idea of the system consists of selecting an appropriate set of actions to be executed, with regard to the current state specification and the control goal given. An abstract mathematical model of the control process is formulated and a suitable language for knowledge representation is proposed. The reasoning scheme is discussed and the structure of the control system is outlined. A representative application example is provided.
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O'Keefe, R., Simulation and expert systems — a taxonomy and some examples, Simulation 46, 10–16 (1986).
Iserman, R., Preprints of the 10th IFAC World Congress, Vol. 7, Munich (1987).
Åström, K.J., Anton, J.J. and Arzen, K.-E., Expert control, Automatica 22, 277–286 (1986).
Tzafestas, S., AI techniques in control: an overview, Reprints of the IMACS Int. Symposium on AI, Expert Systems and Languages in Modelling and Simulation, Barcelona (1987), pp. 55–67.
Tzafestas, S. and Ligęza, A., Expert control through decision making, Reprints of the IMACS Int. Symposium on AI, Expert Systems and Languages in Modelling and Simulation, Barcelona (1987), pp. 91–96.
Ligęza, A., An Expert systems approach to analysis and control in certain complex systems, Preprints of the 4-th IFAC/IFIP Symposium for Computer Control SOCOCO '86, Graz (1986), pp. 147–152.
Ligęza, A., Knowledge engineering bases for expert systems, Proceedings of the Second National Polish Seminar on Artificial Intelligence, Warsaw, (1987).
Pease, M.C., ACS. 1: An experimental command support system, IEEE Trans. Systems Man. Cybernet. 8, 725–735 (1978).
Barr A. and Feigenbaum E., (eds.), The Handbook of Artificial Intelligence, Vol. II, William Kaufmann, Los Altos, California (1982).
Tzafestas, S., Knowledge-Based System Diagnosis, Supervision and Control, Plenum, New York (1989).
Clocksin, W.F. and Mellish, C.S., Programming in Prolog (second edn), Springer-Verlag, Berlin (1984).
Clark, K.L. and McCabe, F.G., PROLOG: a language for implementing expert systems, in J.E. Hayes, D. Michie and Y.-H. Pao (eds.) Machine Intelligence 10, Ellis Horwood, Chichester (1982), pp. 455–470.
Szuba, T., PC-PROLOG for process control applications, Angew. Informatik (Applied Informatics) 4, 164–171 (1984a).
Szuba, T., PROLOG as a real time language for process control, Angew. Informatik (Applied Informatics) 9, 370–374 (1984b).
Chang, C.L. and Lee, R.C.T., Symbolic Logic and Mechanical Theorem Proving, Academic Press, New York, London (1973).
Nilsson, N.J., Principles of Artificial Intelligence, Tioga Pub. Co., Palo Alto, California (1973).
Rich, E., Artificial Intelligence, McGraw-Hill, Singapore (1983).
Fikes, R.E. and Nilsson, N.J., STRIPS: a new approach to the application of theorem proving to problem solving, Artificial Intelligence 2, 189–208 (1971).
Leith, P., Hierarchically structured production rules, The Computer Journal, 26, No. 1, 1–5 (1983).
Barski, J., An automatic control system for sinking and hoisting operations on a laboratory model of a drilling rig with the use of MERA 400 computer: Design and implementation, Master Thesis, Academy of Mining and Metallurgy, Krakow (in Polish), 1987.
Ligęza, A., Time-optimal control of the process of sinking drilling instrument, Scientific Bull. Acad. Mining Metallurgy, No. 867, Automatica 24, Krakow (in Polish) (1982).
Dietterich, T.G. and Michalski, R.S., Inductive learning of structural descriptions: evaluation criteria and comparative review of selected methods, Artificial Intelligence 16, 257–294 (1981).
Michalski, R.S. and Chilausky, R.L., Knowledge acquisition by encoding expert rules versus computer induction from examples: a case study involving soybean pathology. Int. J. Man-Machine Studies 12 63–87 (1980).
Weiss, S.M. and Kulikowski, C.A., A Practical Guide to Designing Expert Systems, Chapman and Hall, London (1984).
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Tzafestas, S., Ligęza, A. A framework for knowledge-based control. J Intell Robot Syst 1, 407–425 (1989). https://doi.org/10.1007/BF00126469
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DOI: https://doi.org/10.1007/BF00126469