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Artificial intelligence for production engineering: a historical approach*

Published online by Cambridge University Press:  09 March 2009

Igor Aleksander
Igor Aleksander
Affiliation:
Dept. of Computing, Imperial College, 80 Queen's Gate, London SW7 2BZ, (U.K.)
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Summary

This paper describes the principles of the advanced programming techniques often dubbed Artificial Intelligence involved in decision making as may be of some value in matters related to production engineering. Automated decision making in the context of production can adopt many aspects. At the most obvious level, a robot may have to plan a sequence of actions on the basis of signals obtained from changing conditions in its environment. These signals may, indeed, be quite complex, for example the input of visual information from a television camera.

At another level, automated planning may be required to schedule the entire work cycle of a plant that includes many robots as well as other types of automated machinery. The often-quoted dark factory is an example of this, where not only some of the operations (such as welding) are done by robots, but also the transport of part-completed assemblies is automatically scheduled as a set of actions for autonomic transporters and cranes. It is common practice for this activity to be preprogrammed to the greatest detail. Automated decision making is aimed at adding flexibility to the process so that it can absolve the system designer from having to forsee every eventuality at the design stage.

Frequent reference is made in this context to artificial intelligence (AI), knowledge-based and expert systems. Although these topics are more readily associated with computer science, it is the automated factory, in general, and the robot, in particular, that will benefit from success in these fields. In this part of the paper we try to sharpen up this perspective, while in part II we aim to discuss the history of artificial intelligence in this context. In part III we discuss the industrial prospects for the field.

Keywords

Production engineeringA.I.HistoricalIntelligence levels
Type
Article
Information
Robotica , Volume 5 , Issue 2 , April 1987 , pp. 99 - 110
Copyright
Copyright © Cambridge University Press 1987

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References

1.Engelberger, J., Robotics in Practice (Kogan Page, London, 1980).Google Scholar
2.Burks, A. W., Goldstine, H.M. and Neumann, J. Von, A Preliminary Discussion of the Logical Design of an Electronic Computing Instrument (Princeton Institute of Advanced Study, 1946).Google Scholar
3.Parent, P. and Laurgeau, C., Logic and Programming Robot Technology vol. 5 (Kogan Page, London, 1983).Google Scholar
4.Feigenbaum, E. A. and McCorduck, P., The Fifth Generation (Addison Wesley, New York, 1983).Google Scholar
5.Minsky, M., “A framework for representing knowledge” In: Winston, P.H. (ed.) The Psychology of Computer Vision (McGraw-Hill, New York, 1975) pp. 211–277.Google Scholar
6.McCarthy, J., “Programs with common sense” Symp. Proc. NPL (HMSO, London, 1958).Google Scholar
7.Boden, M., Artificial Intelligence and Natural Man (Harvester Press, Hassocks, 1977).Google Scholar
8.Nilsson, N. J., Principles of Artificial Intelligence (Tioga Press, Palo Alto, 1980).Google Scholar
9.Shannon, C. E., “Programming a computer for playing chess” Phil. Mag. (series 7)41, 256–275.CrossRefGoogle Scholar
10.Samuel, A. L., “Some studies in machine learning using the game of checkers” IBM J. of R.&D. 3, 211–229 (1959).Google Scholar
11.Holte, R. C., “Artificial intelligence approaches to concept learning” In: Aleksander, I. (ed.) Advanced Digital Information Systems (Prentice-Hall Intern., London, 1985).Google Scholar
12.Newell, J., Shaw, J. and Simon, H., “Report on a general problem solving program for a computer” Proc. Intern. Conf. Information Processing (Unesco, Paris, 1960) pp. 256–264.Google Scholar
13.Fikes, R. E. and Nilsson, N.J., “STRIPS: a new approach to the application of theorem proving to problem solving” Artificial Intelligence 2, Nos. 3–4, 189–208 (1971).CrossRefGoogle Scholar
14.McCarthy, J. et al. , Lisp 1.5 Programming Manual (MIT Press, Cambridge, Mass., 1965).Google Scholar
15.Kowalski, R. A., Logic for Problem Solving (NorthHolland, London, 1979).Google Scholar
16.Aleksander, I. (ed.), Artificial Vision for Robots (Kogan Page, London, 1983).CrossRefGoogle Scholar
17.Roberts, L. G., “Machine perception of 3-D solids” In: Tippett, et al. (eds.) Optical and Electro-optical Information Processing (MIT, Cambridge, Mass., 1965) pp. 159–198.Google Scholar
18.Guzman, A., “Decomposition of a visual field into 3-D objects” In: Grasselli, A., ed. Automatic Interpretation and Classification of Images (Academic Press, London, 1969) pp. 243–276.Google Scholar
19.Marr, D., Vision (Freeman, New York, 1982).Google Scholar
20.Barlow, H., “Understanding natural vision” In: Sleigh, A.C. and Braddick, O. (eds.) The Physical and Natural Processing of Visual Signals (Springer, Heidelberg, 1982).Google Scholar
21.Rosenblatt, F., Principles of Neurodynamics (Spartan, Washington D.C., 1982).Google Scholar
22.Hinton, G., “Learning in parallel networks” Byte 10, No. 4, 265–273 (04, 1985).Google Scholar
23.Winograd, T., Understanding natural Language (Edinburgh Univ. Press, Edinburgh, 1972).CrossRefGoogle Scholar
24.Addis, T. R., Designing Knowledge-based Systems (Kogan Page, London, 1985).Google Scholar