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Artificial Intelligence in Real-Time Control 1989: by M. G. Rodd

By M. G. Rodd

Papers awarded on the workshop are consultant of the state-of-the artwork of man-made intelligence in real-time keep an eye on. the problems lined integrated using AI equipment within the layout, implementation, checking out, upkeep and operation of real-time keep watch over platforms. whereas the focal point was once at the basic features of the methodologies and applied sciences, there have been a few functions papers which helped to place rising theories into point of view. The 4 major matters have been architectural matters; wisdom - acquisition and studying; options; and scheduling, tracking and administration.

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Extra info for Artificial Intelligence in Real-Time Control 1989: Proceedings of the Ifac Workshop, Shenyang, People's Republic of China, 19-21 September 1989

Sample text

E. the addition or assertion of a piece of information may invalidate another). Clearly, in real-time systems, this is a continual condition since variables change as the process progresses in time. Generally, the method used to handle non-monotonicity is to implement schemes such as dependency-directed backtracking, (Doyle, 1979), which try to maintain consistency in the system. The task of propagating changes through the system is non-trivial and time-consum­ ing. Validity intervals can be used to maintain consistency.

1983). Planning Using a Temporal W o r l d M o d e l . Proc. J. 741-747. -E. Control. Automática, (1986). Expert Vol 22, N u m b e r 3. , S m i t h , D . M . J. (1984). Interfac­ ing an Intelligent Decision-Maker to a R e a l - T i m e Control System. 60-64. G e v a r t e r , W . B . (1984). In Artificial Systems Computer Intelligence, Vision and Natural Expert Language Processing. Noyes Publications, N e w Jersey. , H a w k i n s o n , L . , Knickerbocker,C. and C h u r c h m a n , ! (1984) A Real-time E x p e r t System for Process C o n ­ trol.

Some of the DIOs implemented include a forwardchaining monitoring expert system, a backward-chaining diagnostic expert system, a back-propagation multi-layer neural network (supervized learning), a regularity detection neural-network (unsupervized learning). These systems are used to test coordination and cooperation strategies between the DIOs as well as the feasibility of neural networks for real-time control. One of the lessons learnt so far is that such a testbed is invaluable because it tests whether the concepts and theories are practical, efficient and feasible.

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