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Applying decision tree methodology for rules extraction under cognitive constraints. (English) Zbl 1091.90522

Summary: Within this paper the initialization phase of the cognitive management for anthropocentric production systems (COMAPS) project (BRITE-EURAM BE 96-3941) is presented. The entire COMAPS-system is an online decision support system to control dynamically changing industrial production processes. This paper focuses on the adoption of production rules produced by decision tree techniques to the cognitive model of expert’s control practice. The applied algorithms to obtain a quasi-optimal rule set as a model of the intentional representation of the expert knowledge is presented and tested on real world applications (printed circuit board, foil and brake pad production). The results of one application are shown.

MSC:

90B50 Management decision making, including multiple objectives
91E10 Cognitive psychology
68T35 Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence

Software:

C4.5
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References:

[1] G. Coppin, et al., Project program, BRITE-EURAM Project BE 96- 3941; G. Coppin, et al., Project program, BRITE-EURAM Project BE 96- 3941
[2] Barthelémy, J. P.; Mullet, E., Choice basis: A model for multiattribute preference, British Journal of Mathematical and Statistical Psychology, 39, 106-124 (1986) · Zbl 0606.62129
[3] Barthelémy, J. P.; Mullet, E., A model of selection by aspects, Acta Psychologica, 79, 1-19 (1992)
[4] Clark, P.; Niblett, T., The Cn2 induction algorithm, Machine Learning, 3, 261-283 (1989)
[5] T. Scheffer, Learning rules with nested exceptions, in: J. Zizka, P. Brazdil, (Eds.), Proceedings of the International Workshop on Artificial Intelligence Techniques, Brno, 1995, pp. 203-210; T. Scheffer, Learning rules with nested exceptions, in: J. Zizka, P. Brazdil, (Eds.), Proceedings of the International Workshop on Artificial Intelligence Techniques, Brno, 1995, pp. 203-210
[6] W. Müller, F. Wysotzki, Automatic construction of decision trees for classification, in: K. Moser, M. Schader (Eds.), Annals of Operation Research, vol. 92, J.C. Baltzer AG Sciences Publishers, Wijdenes, Netherlands, 1994; W. Müller, F. Wysotzki, Automatic construction of decision trees for classification, in: K. Moser, M. Schader (Eds.), Annals of Operation Research, vol. 92, J.C. Baltzer AG Sciences Publishers, Wijdenes, Netherlands, 1994 · Zbl 0812.90083
[7] Müller, W.; Wysotzki, F., The decision tree algorithm CAL5 based on a statistical approach to its splitting algorithm, (Nakhaizadeh, G.; Taylor, C. C., Machine Learning and Statistics, The Interface (1997), Wiley: Wiley New York)
[8] Quinlan, J. R., C4.5 Programs for Machine Learning (1993), Morgan Kaufmann: Morgan Kaufmann Los Altos, CA
[9] J.R. Quinlan, Generating production rules from decision trees, in: Proceedings IJCAI - Milan, Italy, 1987; J.R. Quinlan, Generating production rules from decision trees, in: Proceedings IJCAI - Milan, Italy, 1987
[10] W. Buntine, R. Caruana, Introduction to IND and recursive partitioning, User’s manual, NASA Ames Research Center, 1991; W. Buntine, R. Caruana, Introduction to IND and recursive partitioning, User’s manual, NASA Ames Research Center, 1991
[11] Michie, D.; Spiegelhalter, D. J.; Tailor, C. C., Machine Learning, Neural and Statistical Classification (1994), Ellis Horwood: Ellis Horwood Hertfordshire, UK · Zbl 0827.68094
[12] R. Andrews, S. Geva, Rule extraction from local cluster neuro nets, Neurocomputing, forthcoming. http://sky.fit.edu.au/∼robert/papers/papers.html; R. Andrews, S. Geva, Rule extraction from local cluster neuro nets, Neurocomputing, forthcoming. http://sky.fit.edu.au/∼robert/papers/papers.html · Zbl 1006.68738
[13] Tickle, A. B.; Andrews, R.; Golea, M.; Diederich, J., The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks, IEEE Transactions on Neural Networks, 9, 6, 1057-1068 (1998)
[14] M. Craven, Extracting comprehensible models from trained neural networks, Ph.D. Thesis, University of Wisconsin, Madison WI, 1996; M. Craven, Extracting comprehensible models from trained neural networks, Ph.D. Thesis, University of Wisconsin, Madison WI, 1996
[15] E. Le Saux, P. Lenca, J.-P. Barthélemy, P. Picouet, Updating a rules base under cognitive constraints: The COMAPS tool, in: 17th European Annual Conference on Human Decision Making and Manual Control, Valenciennes - 12/1998; E. Le Saux, P. Lenca, J.-P. Barthélemy, P. Picouet, Updating a rules base under cognitive constraints: The COMAPS tool, in: 17th European Annual Conference on Human Decision Making and Manual Control, Valenciennes - 12/1998
[16] P. Saunier, R. Bisdorff, Man machine interface for a decision checking tool, in: P. Lenca (Ed.), Proceedings of HCP’99, Brest, France, 1999; P. Saunier, R. Bisdorff, Man machine interface for a decision checking tool, in: P. Lenca (Ed.), Proceedings of HCP’99, Brest, France, 1999
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