Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-23T22:57:05.102Z Has data issue: false hasContentIssue false

How robotics expands A.I.

Published online by Cambridge University Press:  09 March 2009

Alex M. Andrew
Affiliation:
Viable Systems, Splatt Mill, Chillaton, Lifton, Devon PL16 0JB, (U.K.)

Summary

Artificial Intelligence clearly influences Robotics, but the advent of the latter alters the character of A.I. itself, bringing it closer to natural intelligence. This is partly due to greater attention to processes depending on continuous variables, and the combination of these with concept-based or “logical” processes. Some fundamental A.I. principles, notably Minsky's heuristic connection, involve continuity, and the advent ofRobotics should stimulate developments which take them into account. A scheme for a robot which can increase its speed of operation by a learning process is outlined.

Type
Article
Copyright
Copyright © Cambridge University Press 1987

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Selfridge, O.G. and Neisser, U., “Pattern recognition by machine” In: Computers and Thought eds. Feigenbaum, E.A. and Feldman, J. (McGraw-Hill, New York, 1963) pp. 237250. (Originally in Scientific American, August 1960.)Google Scholar
2.Arbib, M.A. and Iberall, T., “Coordinated control programs for movements of the hand” COINS Technical Report 83–25 (University of Massachusetts, Aug. 1983). To be published in Exp. Brain Res. Supplement.Google Scholar
3.Iberall, T. and Lyons, D., “Towards perceptual robotics” COCMS Tech. Rep. (University of Massachusetts, August, 1984). Presented at IEEE Conference on SMC.Google Scholar
4.Andrew, A.M., Artificial Intelligence (Abacus, Tunbridge Wells, 1983) p. 139.Google Scholar
5.Andrew, A.M., “Elementary continuity and Minsky's Heuristic Connection”. Presented at Second International Conference on Artificial Intelligence,Repino, near Leningrad (1980).Google Scholar
6.Minsky, M., “Steps toward artificial intelligence” In: Computers and Thought eds. Feigenbaum, E.A. and Feldman, J. (McGraw-Hill, New York, 1963) pp. 406450.Google Scholar
7.Minsky, M.L., contribution to discussion. In: Mechanisation of Thought Processes (H.M.S.O. London 1959) p. 71.Google Scholar
8.Minsky, M. and Selfridge, O. G., “Learning in randomnets” In: Information Theory ed. Cherry, E.C. (Butter-worth, London 1961) pp. 335347.Google Scholar
9.Selfridge, O.G., “Pandemonium: a paradigm for learning” In: Mechanisation of Thought Processes (H.M.S.O. London 1959) pp. 511531.Google Scholar
10.Frsyth, R., “Beagle – a Darwinian approach to pattern recognitionKybernetes 10, 159166 (1981).CrossRefGoogle Scholar
11.McKay, D.M., “On the combination of digital and analogue computing techniques in the design of analytical engines” In: Mechanisation of Thought Processes (H.M.S.O. London 1959), pp. 5565 (circulated privately in 1949).Google Scholar
12.Andrew, A.M., “Cybernetics and artificial intelligence” In: Modern Trends in Cybernetics and Systems eds. Rose, J. and Bilciu, C. (Editura Technica, Bucharest and Springer, N.Y., 1976) vol. 3, pp. 477–485.Google Scholar
13.Newell, A., Shaw, J.C. and Simon, H., “Report on a general problem-solving programProc. Int. Conf. on Information Processing (UNESCO,Paris, 1959) pp. 256264.Google Scholar
14.Andrew, A.M., “The concept of a concept” In: Applied Systems and Cybernetics 2, ed. Lasker, J. (Pergamon, NewYork, 1981) pp. 607612.Google Scholar
15.Andrew, A.M., “Logic and continuity – a systems dichotomy” In: Cybernetics and Systems Research ed. Trappl, R. (North-Holland, Amsterdam, 1982) pp. 1922.Google Scholar
16.Benati, M., Gaglio, S., Morasso, P., Tagliasco, V. and Zaccoria, R., “Anthropomorphic robotsBiol. Cybernetics 38, 125140 and 141150 (1980).CrossRefGoogle Scholar
17.Hardy, S., “Robot control systems” In: Artificial Intelligence eds. O'Shea, T. and Eisenstadt, M. (Harper and Row, New York, 1984) pp. 178191.Google Scholar
18.Hemami, H. and Zheng, Y.-F., “Dynamics and control of motion on the ground and in the air with application to biped robotsJ. Robotic Systems 1, 101116 (1984).CrossRefGoogle Scholar
19.Vukobratović, M. and Stepanenko, J., “On the stability of anthropomorphic systemsMath. Biosciences 10, 137 (1972).CrossRefGoogle Scholar
20.Danthine, A. and Géradin, M. (eds.), Advanced Software in Robotics (North-Holland, Amsterdam, 1984) (Reviewed in Robotica 3, 193, 1985).Google Scholar
21.Lee, C.S.G., Chung, M.J. and Lee, B.H., “An approach of adaptive control for robot manipulatorsJ. Robotic Systems 1, 2757 (1984).CrossRefGoogle Scholar
22.Gabor, D., Wilby, W.P.L. and Woodcock, R., “A universal non-linear filter, predictor and simulator which optimizes itself by a learning processProc. I.E.E. (London) part B, 13, 422435 (1961).Google Scholar
23.Andrew, A.M., “Learning machines” In: Mechanisation of Thought Processes (H.M.S.O. London 1959) pp. 474505.Google Scholar
24.Pask, A.G., Contribution to discussion Proc. I.E.E. (London) part B, 13, 437 (1961).Google Scholar
25.Strachey, C., Contribution to discussion In: Mechanisation of Thought Processes (H.M.S.O. London 1959) pp. 507–508.Google Scholar
26.Bario, A.G., Sutton, R.S. and Brouwer, P.S. “Associative search network: a reinforcement learning associative memory” COINS Technical Report 80118 (University of Massachusetts, 09 1980).Google Scholar
27.Andrew, A.M., “Some comments on adaptive robotics” In: Artificial Intelligence ed. Ponomaryov, V.M. (Pergamon, Oxford 1984) pp. 239243.Google Scholar
28.Wang, Y.M., “More discussion on the ‘Soft Bionic Man-Made Brain’ controlling the limbs motion of robot” In: Advanced Software in Robotics eds. Danthine, A. and Géradin, M. (North-Holland, Amsterdam, 1984).Google Scholar