Artificial Intelligence In Real Time Control 1998
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Artificial Intelligence in Real-Time Control 1998
This symposium was the seventh in a very successful series in this field. Since the beginning of the series, there have been a number of very positive developments in the topical area of 'Intelligent Control'. In particular, the area referred to as 'situated control' has stimulated the formation of new perspectives towards real-time intelligent systems. The performances of such artificial species as walking cockroaches, maze-negotiating mice, coke-can collecting robots and the like have encouraged the exploration of yet more adaptive control perspectives. In this symposium, there was a strong wind of change bringing more consideration of the roles of learning, evolution, hybrid systems and so on under many diverse labels and for many different systems and circumstances.
Artificial Intelligence in Real-time Control (AIRTC-2000)
This Proceedings contains the papers presented at the 9th IFAC AIRTC'2000 Symposium on Artificial Intelligence in Real-Time Control 2000, held at Budapest Polytechnic, Hungary, on 2 - 4 October. AIRTC'2000 builds on the excellent reputation of previous meetings in the series for providing top-quality papers in this important research field. A positive development illustrated by this Proceedings is a new trend towards pragmatism in the research field. Examples of this trend are: an increase in the number of actual industrial applications; support for more widespread use of new sophisticated technologies (e.g. materials design); further intertwining of artificial intelligence and control theory methods that reduces the reliance on blind faith, still too often associated with AI methods. Many things have changed since the first AIRTC event in 1988. Two examples illustrate the change in the general attitude of the IFAC family: in 1990, one of the major closing presentations of the IFAC World Congress warned the control community about the coming hordes of AI people. In 1999, one of the plenary papers at the IFAC World Congress pointed out that the AI based methods form a natural extension of control theory to the class of non-linear systems with incomplete information (at least as far as the optimisation is concerned). This contrast in attitudes shows how, during the past decade, many AI people have embraced control theory and many control people have learned the basics of AI. This Proceedings serves to continue this excellent dialogue, by providing many quality papers which link both fields.
The BOXES Methodology
Author: David W. Russell
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-03-14
Robust control mechanisms customarily require knowledge of the system’s describing equations which may be of the high order differential type. In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior. There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent. Several years ago a new genre of control systems came to light that are much less dependent on differential models such as fuzzy logic and genetic algorithms. Both of these soft computing solutions require quite considerable a priori system knowledge to create a control scheme and sometimes complicated training program before they can be implemented in a real world dynamic system. Michie and Chambers’ BOXES methodology created a black box system that was designed to control a mechanically unstable system with very little a priori system knowledge, linearization or approximation. All the method needed was some notion of maximum and minimum values for the state variables and a set of boundaries that divided each variable into an integer state number. The BOXES Methodology applies the method to a variety of systems including continuous and chaotic dynamic systems, and discusses how it may be possible to create a generic control method that is self organizing and adaptive that learns with the assistance of near neighbouring states. The BOXES Methodology introduces students at the undergraduate and master’s level to black box dynamic system control , and gives lecturers access to background materials that can be used in their courses in support of student research and classroom presentations in novel control systems and real-time applications of artificial intelligence. Designers are provided with a novel method of optimization and controller design when the equations of a system are difficult or unknown. Researchers interested in artificial intelligence (AI) research and models of the brain and practitioners from other areas of biology and technology are given an insight into how AI software can be written and adapted to operate in real-time.