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169 Learning to avoid collisions: A reinforcement learning paradigm for mobile robot navigation
Abstracts novel method of synthesizing the optimal control is developed. The neural network learns the unknown dynamics of a nonlinear plant with arbitrary order, then is expanded into an equivalent feedforward multilayer network. The gradient of a criterion functional is obtained from this multilayer network, and the optimal control is generated by applying an existing nonlinear programming algorithm. The proposed method is successfuUy applied to the optimal control synthesis problem of a nonlinear coupled vibratory plant with a linear quadratic criterion functional.
169 Learning to Avoid Collisions: A Reinforcement Learning Paradigm for Mobile Robot Navigation B.J.A. Krtse, J.W.M. van Dam, pp 317-322 The paper describes a self-learning control system for a mobile robot. Based on sensor information, the control system has to provide a steering signal in such a way that collisions are avoided. Since in the case described, no "examples" are available, the system learns on the basis of an external reinforcement signal which is negative in the case of a collision and zero otherwise. The paper describes the adaptive algorithm which is used for a discrete coding of the state space, and the adaptive algorithm for learning the correct mapping from the mput (state) vector to the output (steering) signal. 170 Genetic Algorithms for Process Control: A Survey J.M. Renders, J.P. Nordvlk, H. Bersinl, pp 323-328 This paper presents a survey of the potential use of Genetic Algorithms (GAs) for process control. GAs are a family of iterative search algorithms based on an analogy with the process of natural selection and evolutionary genetics. Application to off-line control is envisaged, where GAS are used for task scheduling, calculation of optimal setpoints and design of optimal control strategies. Then application to on-line control is considered, focusing on system identification and enhancement of existing controllers. After the description of possible applications of GAS to supervisory problems, the general advantages, drawbacks and limitations of applying GAs to process control are discussed, and further lines of research are drawn.
1095 for a fuzzy logic controller, using a version of the rule competition production systems (Classifier Systems), where rules are matched in the fuzzy domain rather than as binary patterns. Only one structure from a population is evaluated in each time interval. To hasten learning, the payoff received assigns estimates of new strength to the other classifiers. Rule learning is initialized with randomly generated structures, plus fairly general heuristic knowledge. The interacting environment was modelled by a real-time simulation of closed-loop anaesthetic drug administration, but the environments characteristics are not known to the GA. 173 Automated Synthesis of Control for Nonlinear Dynamic Systems T. Urbancic, D. Juricic, B. Filipic, I. Bratko, pp 341-346 Artificial intelligence methods appear to be particularly well-suited for control design when only inexact prior knowledge about the system to be controlled is available. Design tasks that can be solved include learning control from scratch, improving partial control knowledge, and controller tuning. The paper enlightens these approaches in two case studies, both dealing with nonlinear unstable systems: inverted pendulum control, and position control of a floating object. Comparison to the classical modelbased control design approaches is also provided. 174 On Representations for Continuous Dynamic Systems E.A. Woods, pp 347-352 Through the use of a simple example involving a buffer tank, the paper illustrates the problem of attempting to formalize the dynamic aspects of a continuous process system, particularly when using a purely qualitative representation. Where it is necessary to make predictions from a model of a dynamic system, a model based on a quantitative mathematical representation is needed. Since representations of this type will not support reasoning about the system, apart from the computation of related values for variables, some kind of hybrid approach must be employed, to make predictions about the evolution of variables while retaining the ability to reason about the behaviour of the system.
171 An Adaptive System for Process Control using Genetic Algorithms C.L. Karr, pp 329-334
175 Process Knowledge Acquisition and Control by Quantitative and Qualitative Complementarity T. Nakagawa, Y. Sawaragi, Y. Yagihara, pp 353-358
The paper describes adaptive process control systems in which genetic algorithms (GAs) augment fuzzy logic controllers (FLCs). GAs rapidly locate near-optimum solutions to a wide spectrum of problems by modelling the search procedures of natural genetics. FLCs are rulebased systems that efficiently manipulate a problem environment by modelling the "rule of thumb" strategy used in human decision-making. Together, they possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems, comprising a control element, an analysis element, and a learning e/ement. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
In computer control based on an autoregressive model. the subsequent measured values are sometimes imperfect due to disturbances and noise. This paper proposes to overcome this drawback by using model-based deep knowledge based on an existing AR model or mathematical model, and converting it to fuzzy qualitative control. An actual example of a cement rotary kiln process is given. Process disturbances and incomplete measured values are handled by transforming quantitative control into qualitative control, and making use of hidden information. A feature of this method is the paradigm which does not quantify a qualitative model, but rather goes in the opposite direction of qualitizing a quantitative model.
172 Real-Time Acquisition of Fuzzy Rules using Genetic Algorithms D.A. Linkens, H.O. Nyongesa, pp 335-340
176 Model-Based Diagnosis. State Transition Events and Constraint Equations A. Nilsson, K.-E. Arz~n, T.F. Petti, pp 359-364
The paper presents a Genetic Algorithm (GA)-based system for on-line acquisition and modification of rules
Two different approaches to model-based on-line diagnosis are compared. The DMP method is based on
169 Learning to Avoid Collisions: A Reinforcement Learning Paradigm for Mobile Robot Navigation B.J.A. Krtse, J.W.M. van Dam, pp 317-322 The paper describes a self-learning control system for a mobile robot. Based on sensor information, the control system has to provide a steering signal in such a way that collisions are avoided. Since in the case described, no "examples" are available, the system learns on the basis of an external reinforcement signal which is negative in the case of a collision and zero otherwise. The paper describes the adaptive algorithm which is used for a discrete coding of the state space, and the adaptive algorithm for learning the correct mapping from the mput (state) vector to the output (steering) signal. 170 Genetic Algorithms for Process Control: A Survey J.M. Renders, J.P. Nordvlk, H. Bersinl, pp 323-328 This paper presents a survey of the potential use of Genetic Algorithms (GAs) for process control. GAs are a family of iterative search algorithms based on an analogy with the process of natural selection and evolutionary genetics. Application to off-line control is envisaged, where GAS are used for task scheduling, calculation of optimal setpoints and design of optimal control strategies. Then application to on-line control is considered, focusing on system identification and enhancement of existing controllers. After the description of possible applications of GAS to supervisory problems, the general advantages, drawbacks and limitations of applying GAs to process control are discussed, and further lines of research are drawn.
1095 for a fuzzy logic controller, using a version of the rule competition production systems (Classifier Systems), where rules are matched in the fuzzy domain rather than as binary patterns. Only one structure from a population is evaluated in each time interval. To hasten learning, the payoff received assigns estimates of new strength to the other classifiers. Rule learning is initialized with randomly generated structures, plus fairly general heuristic knowledge. The interacting environment was modelled by a real-time simulation of closed-loop anaesthetic drug administration, but the environments characteristics are not known to the GA. 173 Automated Synthesis of Control for Nonlinear Dynamic Systems T. Urbancic, D. Juricic, B. Filipic, I. Bratko, pp 341-346 Artificial intelligence methods appear to be particularly well-suited for control design when only inexact prior knowledge about the system to be controlled is available. Design tasks that can be solved include learning control from scratch, improving partial control knowledge, and controller tuning. The paper enlightens these approaches in two case studies, both dealing with nonlinear unstable systems: inverted pendulum control, and position control of a floating object. Comparison to the classical modelbased control design approaches is also provided. 174 On Representations for Continuous Dynamic Systems E.A. Woods, pp 347-352 Through the use of a simple example involving a buffer tank, the paper illustrates the problem of attempting to formalize the dynamic aspects of a continuous process system, particularly when using a purely qualitative representation. Where it is necessary to make predictions from a model of a dynamic system, a model based on a quantitative mathematical representation is needed. Since representations of this type will not support reasoning about the system, apart from the computation of related values for variables, some kind of hybrid approach must be employed, to make predictions about the evolution of variables while retaining the ability to reason about the behaviour of the system.
171 An Adaptive System for Process Control using Genetic Algorithms C.L. Karr, pp 329-334
175 Process Knowledge Acquisition and Control by Quantitative and Qualitative Complementarity T. Nakagawa, Y. Sawaragi, Y. Yagihara, pp 353-358
The paper describes adaptive process control systems in which genetic algorithms (GAs) augment fuzzy logic controllers (FLCs). GAs rapidly locate near-optimum solutions to a wide spectrum of problems by modelling the search procedures of natural genetics. FLCs are rulebased systems that efficiently manipulate a problem environment by modelling the "rule of thumb" strategy used in human decision-making. Together, they possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems, comprising a control element, an analysis element, and a learning e/ement. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
In computer control based on an autoregressive model. the subsequent measured values are sometimes imperfect due to disturbances and noise. This paper proposes to overcome this drawback by using model-based deep knowledge based on an existing AR model or mathematical model, and converting it to fuzzy qualitative control. An actual example of a cement rotary kiln process is given. Process disturbances and incomplete measured values are handled by transforming quantitative control into qualitative control, and making use of hidden information. A feature of this method is the paradigm which does not quantify a qualitative model, but rather goes in the opposite direction of qualitizing a quantitative model.
172 Real-Time Acquisition of Fuzzy Rules using Genetic Algorithms D.A. Linkens, H.O. Nyongesa, pp 335-340
176 Model-Based Diagnosis. State Transition Events and Constraint Equations A. Nilsson, K.-E. Arz~n, T.F. Petti, pp 359-364
The paper presents a Genetic Algorithm (GA)-based system for on-line acquisition and modification of rules
Two different approaches to model-based on-line diagnosis are compared. The DMP method is based on