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Machine Learning in a Dynamic World P. J. Antsaklis, "Learning in Control,” in Panel Discussion on "Machine Learning in a Dynamic World" (Invited), M.M.Kokar Organizer, P roc. o f t he 3 rd I EEE I ntern. S ymposium o n I ntelligent C ontro l , pp. 500- 507, Arlington, VA, Aug. 24-26, 1988. MACHINE LEARNING IN A DYNAMIC WORLD Panel Dmussion Miecsyslaw M. Kohr Panel O&m and Editor Northeastern Univdty Boston, Massachwtts Panelists: P. J. Atnsaklis, University of Notre Dame K. A. DeJong, George Mason University A. L. Meyrowitz, Office of Naval Research A. Meystel, Drexel University R. S. Michal~gi,George Mason University R. S. Sutton, GTE Laboratories Machine Learning was established as a research discipline in the lems while ignoring common-senseknowledge. How much of progresd 1970's and experienced a growth expansion in the 1980's. One of in controlling real-world systems can be made without taking into the roots of machine learning research was in Cybernetic Systems account all kinds of imprecise information? and Adaptive Control. Machine Learning has been significantly influenced by Artificial Intelligence, Cognitive Science, Computer In the following we present the views of the panelists. This pre- Science, and other disciplines; Machine Learning has developed its sentation is based on the written material submitted to the panel own research paradigms, methodologies, and a set of research ob- organizers by the panelists. The preeentation has been divided jectives different from those of control systems. In the meantime, a into four sections. The first section is devoted to the trends in the new field of Intelligent Control has emerged. Even though Intelli- development of the two disciplines, Machine Learning and Intelli- gent Control adheres more closely to the traditional control systems gent Control. In the second section we present the panelists' views theory paradigms - mainly quantitative descriptions, differential on the main p~adigm~within the disciplines. The following equations models, goal-oriented system design, rigid mathematical two section contains the common challenges to the both communities formulation of goals and models - it has also deviated from the which occur a result of the need to control complex dynamic traditional systems theory approach. as systems. Finally, in the last section some similarities between the The two fields have moved forward without much interaction research issues and methods in the two disciplines are identified. between them - different conferences, different journals, different researchers. Machine Learning has been concerned primarily with MAIN TRENDS IN MACHINE LEARNING general learning mechanisms and methodologies for software imple- and INTELLIGENT CONTROL mentation of learning systems. Intelligent Control has concentrated more on the dynamics of real physical systems and practical control Antsaklis Notes from the past. In the 60'5, adaptive control problem solving. Because the two disciplines have at least me goal and learning received a lot of attention in the control literature. in common - automatic acquisition of knowledge about the world It was not always clear, however, what was meant by those terms. - they should have more interaction. The lack of interdisciplinary The comment by Y.Tsypkin (1971) describes quite clearly the at- communication may lead to some undesirable results: establishing mosphere of the period, which, I should say, also has some striking different terminologies for the same phenomena, repetitive work similarities with the today's atmosphere: (discovering the same things independently), and lower quality re- "It is difficult to find more fashionable and attractive terms in search (ignoring the results established by the other discipline). the modern theory of automatic control than the terms of adapta- The goal of this panel was to analyze the interactions between tion and learning. At the same time, it is not simple to find any Machine Learning and Intelligent Control. The panel consisted of other concepts which are less complex and more vague." several researchers both from the area Intelligent Control and of Adaptation, learning, self-organizing systems and control were from Machine Learning. competing terms for similar research areas, and K. S. Fu says The panelists were asked to concentrate on such general issues (1970): as: "The use of the word 'adaptive' has been intentionally avoided - the need for the interaction, here ... adaptive and learning are behavior-descriptive terms, but feedback and self-organizing are strueture, or system configuration, - common research topics, descriptive terms. Nevertheless the terminology war is still going - common results, on .... It is certainly not the purpose of this paper to get involved with such a war." - common methods. The term pattern recognition was appearing together with The more specific topic this panel was machine learning also of adaptive, learning and self-organizing systems in the control liter- and world dynamics. ature of that era. It is obvious that there was no agreement as to - Machine Learning devotes very little attention to the dynam- the meaning of these terms and their relation. ics of real world. How does the lack of dynamics in the machine Today, twenty or more years later, we have made some progress, learning models affect the practical value the machine learning of at least in agreeing about the meaning of certain terms and we have results? come full cycle in the popularity of certain research areas. Certainly - Intelligent Control concentrates on formally specified prob- pattern recognition is today a research discipline in its own right, 0-8486-2012-9/89/0000/0500$01 .OO 0 1989 IEEE 500 Authorized licensed use limited to: UNIVERSITY NOTRE DAME. Downloaded on October 20, 2009 at 15:28 from IEEE Xplore. Restrictions apply. P. J. Antsaklis, "Learning in Control,” in Panel Discussion on "Machine Learning in a Dynamic World" (Invited), M.M.Kokar Organizer, P roc. o f t he 3 rd I EEE I ntern. S ymposium o n I ntelligent C ontro l , pp. 500- 507, Arlington, VA, Aug. 24-26, 1988. developing and using an array of methods ranging from conven- Initially it was clear that the only thing ae do no1 knob is thv tional algorithms to artificial intelligence methods implemented via external world. Thus, for K.S.Fu (1971) the term learning controller symbolic processing. The term selforganizing systems has almost meant equipping the controller with a set of devices with human-like disappeared from use in the control literature. Adaptive control capabilities, i.e., pattern recognition and decision making. These has gained renewed qopularity in the past decade mainly empha- devices were to learn the reality. For Y.Tsypkin (1971) the system sizing studies in the convergence of adaptive algorithms and in the of learning consisted of a preassigned set of operations, and the stability of adaptive systems; the systems considered are primar- control inputs were to be learned: ”Under the term learning in a ily systems described by differential (or difference) equations where system, we shall consider a process of forcing the system to have a the coefficients are (partially) unknown. In an attempt to enhance particular response to a specific input signal (action) by repeating the applicability of adaptive control methods, learning control has the input signals and then correcting the system externally.” The been recently reintroduced in the control literature. subsequent two decades can be considered a period of clarification Evolution of control systems. I consider the introduction of and establishing a new scientific paradigm which is suggested by learning in control as part of the continuing evolution of the cou- intelligent control. trol methods to address more complicated and demanding control Meyrowitz Research in machine learning, control, and their problems. integration is sponsored by the Office of Naval Research within Intelligent Systems Program. That Program has two Typically, control systems are dynamic systems and they in- the ONR volve feedback mechanisms. The system to be controlled, usually primary components: Artificial Intelligence, where the objective called the plant, and the decision making controller are distinct is to understand automation and extension of human intellectual entities and they are both described by differential or difference skills, and Robotics, where the concern is with understanding t11r equations. Conventional control systems are designed using math- design of intelligent sensor-based mechanical systems. The common ematical models of physical systems. A mathematical model which element of intelligence creates a broad overlap of research interest captures the dynamical behavior of interest is chosen and then con- across the two areas. As progress is made in artificial intelligence, trol design techniques are applied, aided by CAD packages, to de- we expect to see the discovery of automated techniques crucial to sign the mathematical model of an appropriate controller. The advanced aids to human decision making; at the same time, those controller is then realized via hardware of software and it is used techniques are likely to play an important role in the controlling to control the physical system. The procedure may take several software for intelligent robots. iterations. The mathematical model of the system must be ”sim- The issues of automated inductive learning, reasoning by anal- ple enough” so that it can be analyzed with available mathemat- ogy, and scientific discovery are
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