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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Dennis Barrios-Aranibar Luiz Marcos Garcia Goncalves Department of Computing Engineering and Automation Federal University of Rio Grande do Norte Natal Brazil

In this paper authors propose a new paradigm for learning coordination in multi-agent systems. This approach is based on social interaction of people, specially in the fact that people communicate each other what they think about their actions and this opinion can influence the behavior of each other. It is proposed a model in which agents, into a multi-agent system, learns to coordinate actions giving opinions about actions of other agents and also being influenced with opinions of other agents about their actions. This paradigm was used to develop a modified version of the Q-learning algorithm. This algorithm was tested and compared with independent learning (IL) and joint action learning (JAL) in two single state problems with two agents. This approach shows to have more probability to converge to an optimal equilibrium than IL and JAL Q-learning algorithms. Also, it does not need to make an entire model of all joint actions like JAL algorithms.

关键词： Multiagent systems Learning Nash equilibrium Collaborative work Intelligent systems Design engineering Design automation Testing Robot kinematics Artificial intelligence

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Learning to Reach Optimal Equilibrium by Influence of Other Agents Opinion

Learning to Reach Optimal Equilibrium by Influence of Other ...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： Dennis Barrios-Aranibar Luiz Marcos Garcia Goncalves Department of Computing Engineering and Automation Federal University of Rio Grande do Norte Natal RN Brazil

In this work authors extend the model of the reinforcement learning paradigm for multi-agent systems called "influence value reinforcement learning " (IVRL). In previous work an algorithm for repetitive games was proposed, and it outperformed traditional paradigms. Here, authors define an algorithm based on this paradigm for using when agents has to learn from delayed rewards, thus, an influence value reinforcement learning algorithm for two agents stochastic games. The IVRLparadigm is based on social interaction of people, specially in the fact that people communicate each other what they think about their actions and this opinion has some influence in the behavior of each other. A modified version of Q-learning algorithm using this paradigm was constructed. The so called TV Q-learning algorithm was implemented and compared with versions of Q-learning for independent learning and joint action learning. Our approach shows to have more probability to converge to an optimal equilibrium than IQ-learning and JAQ-learning algorithms, specially when exploration increases.

关键词： Multiagent systems Stochastic processes Nash equilibrium Learning Delay Collaborative work Game theory Hybrid intelligent systems automation Testing

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Novel Architecture for supporting medical decision making of different data types based on Fuzzy Cognitive Map Framework

Novel Architecture for supporting medical decision making of...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Elpiniki Papageorgiou Chrysostomos Stylios Peter Groumpos Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Greece Intelligent Computing Laboratory Department of Informatics and Telecommunications Technology TEI Epirus Greece

Medical problems involve different types of variables and data, which have to be processed, analyzed and synthesized in order to reach a decision and/or conclude to a diagnosis. Usually, information and data set are both symbolic and numeric but most of the well-known data analysis methods deal with only one kind of data. Even when fuzzy approaches are considered, which are not depended on the scales of variables, usually only numeric data is considered. The medical decision support methods usually are accessed in only one type of available data. Thus, sophisticated methods have been proposed such as integrated hybrid learning approaches to process symbolic and numeric data for the decision support tasks. Fuzzy cognitive maps (FCM) is an efficient modelling method, which is based on human knowledge and experience and it can handle with uncertainty and it is constructed by extracted knowledge in the form of fuzzy rules. The FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This rule base could be derived by a number of machine learning and knowledge extraction methods. Here it is introduced a hybrid attempt to handle situations with different types of available medical and /or clinical data and with difficulty to handle them for decision support tasks using soft computing techniques.

关键词： Decision making Fuzzy cognitive maps Medical diagnostic imaging Data mining Decision support systems Learning systems Computational intelligence Computer networks Diseases Spatial databases

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A visual system theoretic cost criterion and its application to clustering and fuzzy modeling

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Information Technology Journal 2007年第2期6卷 310-324页

作者： Wang, Shitong Chung, Fu-Lai Xu, Min Deng, Zhaohong Hu, Dewen School of Information Engineering Southern Yangtze University Wuxi China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong School of Automation National Defense University of Science and Technology Changsha China

We all know that our eyes can inherently and effectively recognize/classify objects under complex conditions. Hence, we believe that an efficient clustering approach not only depends on the principles of physical systems by which the data are generated but also on the manner that human eyes sense the structure of the data. In this study a visual system theoretic cost criterion function is proposed and based upon which a new clustering algorithm is derived. The new cost criterion is visual sampling and Weber's law is applied. The new criterion function can be made "kemelized" so that developed based on a visual system modeling of the multi-dimensional data where the visual system theories like different kernel functions can be used under different practical requirements. Furthermore, it evaluates the tightness of intra-group's data distribution and the separable degree among groups simultaneously. The experimental results demonstrate that the new clustering algorithm is especially suitable for nonlinearly separable datasets. © 2007 Asian Network for Scientific Information.

关键词： Computer vision

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A CAN and OSEK NM based siren for automobiles

A CAN and OSEK NM based siren for automobiles

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2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

作者： Qiao, Xin Wang, Zhixue Sun, Yuan He, Feng Wang, Fei-Yue IEEE Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences CO 100080 Beijing China Institute of Computing Chinese Academy of Sciences CO 100080 Beijing China Key Laboratory for Complex Systems and Intelligence Science Chinese Academy of Sciences Beijing China Program for Advanced Research in Complex Systems Department of Systems and Industrial Engineering University of Arizona Tucson AZ 85719 United States

ISBN: (纸本)1424410762

Today, more and more Electronic Control Units have been installed in vehicle, and Controller Area Network is one of the most widely used networks to connect these ECUs, also the architecture of OSEK is increasingly employed into the design of individual ECU and the network. In this paper, we propose a new framework to design the ECU with limited-storage-space. In the framework, an infinite cycle acts as a mainframe, and the code conforming to OSEK NM is inserted into the cycle. A siren of vehicle is developed with this method, and a Hardware-In-Loop test is accomplished. The results show that the framework is available, and the real-time performance of the siren is satisfied. © 2007 IEEE.

关键词： Intelligent vehicle highway systems

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A Multiobjective Approach to Phylogenetic Trees: Selecting the Most Promising Solutions from the Pareto Front

A Multiobjective Approach to Phylogenetic Trees: Selecting t...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： G.P. Coelho F.J. Von Zuben A.E.A. da Silva Univ. of Campinas - Unicamp Campinas Laboratory of Bioinformatics and Bioinspired Computing-LBiC Department of Computer Engineering and Industrial Automation-DCA School of Electrical and Computer Engineering-FEEC State University of Campinas-UNICAMP Brazil School of Mathematical and Nature Sciences Methodist University of Piracicaba Brazil

This work presents the application of the omni-aiNet algorithm - an immune-inspired algorithm originally developed to solve single and multiobjective optimization problems - to the reconstruction of phylogenetic trees. The main goal of this work is to automatically evolve a population of phylogenetic unrooted trees, possibly with distinct topologies, by minimizing at the same time the minimal evolution and the mean-squared error criteria. The obtained set of phylogenetic trees contains non-dominated individuals that form the Pareto front and that represent the trade-off of the two conflicting objectives. Given this set of phylogenetic trees, two multicriterion decision-making techniques were applied in order to try to select the best solution within the Pareto front.

关键词： Phylogeny Decision making Proposals Application software Design engineering Organisms DNA Sequences Intelligent systems Laboratories

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A Hybrid HMM/ANN Based Approach for Online Signature Verification

A Hybrid HMM/ANN Based Approach for Online Signature Verific...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhong-Hua Quan De-Shuang Huang Kun-Hong Liu Kwok-Wing Chau Institute of Intelligent Machines Chinese Academy of Science Department of Automation University of Science and Technology China Intelligent Computing Lab Institute of Intelligent Machines Chinese Academy and Sciences China Department of Civil & Structural Engineering Hong Kong Polytechnic University Hong Kong China

This paper presents a new approach based on HMM/ANN hybrid for online signature verification. A group of ANNs are used as local probability estimators for an HMM. The Viterbi algorithm is employed to work out the global posterior probability of a model. The proposed HMM/ANN hybrid has a strong discriminant ability, i.e, from a local sense, the ANN can be regarded as an efficient classifier, and from a global sense, the posterior probability is consistent with that of a Bayes classifier. Finally, the experimental results show that this approach is promising and competing.

关键词： Hidden Markov models Handwriting recognition Artificial neural networks Context modeling Machine intelligence Probability Neural networks Viterbi algorithm Writing Automatic speech recognition

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On Automatic and Dynamic Camera Calibration based on Traffic Visual Surveillance

On Automatic and Dynamic Camera Calibration based on Traffic...

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IEEE Symposium on Intelligent Vehicle

作者： Yuantao Li Fenghua Zhu Yunfeng Ai Fei-Yue Wang Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China College of Computing & Communication Engineering Chinese Academy of Sciences Beijing China Department of Systems and Industrial Engineering University of Arizona Tucson Tucson AZ USA Key Laboratory for Complex Systems and Intelligence Science Chinese Academy and Sciences Beijing China

This paper presents a novel automatic and dynamic camera calibration algorithm based on traffic visual surveillance. The algorithm can automatically calibrate the intrinsic and extrinsic parameters of camera, and offer enough guarantee to extract traffic real-time parameters. This paper firstly indicates the significance of applying automatic and dynamic calibration algorithm to intelligent transportation system, then establishes a mathematical model of calibration parameters between camera image and actual road. The algorithm is established from three view points, which are description, flow chart and algorithm description. Afterward this paper describes the experimental method and result based on this novel algorithm. Finally this paper indicates wide application foreground of this algorithm in intelligent transportation system.

关键词： Calibration Surveillance Vehicle dynamics Traffic control Intelligent transportation systems Smart cameras Roads Robot vision systems Educational institutions Layout

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A CAN and OSEK NM Based Siren for Automobiles

A CAN and OSEK NM Based Siren for Automobiles

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IEEE International Conference on Networking, Sensing and Control

作者： Xin Qiao Zhixue Wang Yuan Sun Feng He Fei-Yue Wang Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China Institute of Computing Chinese Academy and Sciences Beijing China Department of Systems and Industrial Engineering University of Arizona Tucson Tucson AZ USA Key Laboratory for Complex Systems and Intelligence Science Chinese Academy and Sciences Beijing China

关键词： Automobiles Electrical equipment industry Industrial control Vehicle safety Read only memory Automotive engineering Distributed control Manufacturing industries Control systems Wiring

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A Study On Iterative Learning Control With Adjustment Of Learning Interval For Monotone Convergence In The Sense Of Sup-Norm

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Asian Journal of Control 2008年第1期4卷

作者： Kwang-Hyun Park Zeungnam Bien Division of EE Department of EECS Korea Advanced Institute of Science and Technology 373–1 Kusong-dong Yusong-gu Taejon 305–701 Korea. Zeungname Bien:received the B.S. degree in electronics engineering from Seoul National University Seoul Korea in 1969 and the M.S. and Ph.D. degrees in electrical engineering from the University of Iowa Iowa City Iowa U.S.A. in 1972 and 1975 respectively. During 1976–1977 academic years he taught as assistant professor at the Department of Electrical Engineering University of Iowa. Then Dr. Bien joined Korea Advanced Institute of Science and Technology summer 1977 and is now Professor of Control Engineering at the Department of Electrical Engineering and Computer Science KAIST. Dr. Bien was the president of the Korea Fuzzy Logic and Intelligent Systems Society during 1990–1995 and also the general chair of IFSA World Congress 1993 and for FUZZ-IEEE99 respectively. He is currently co-Editor-in-Chief for International Journal of Fuzzy Systems (IJFS) Associate Editor for IEEE Transactions on Fuzzy Systems and a regional editor for the International Journal of Intelligent Automation and Soft Computing. He has been serving as Vice President for IFSA since 1997 and is now Chief Chairman of Institute of Electronics Engineers of Korea and Director of Humanfriendly Welfare Robot System Research Center. His current research interests include intelligent control methods with emphasis on fuzzy logic systems service robotics and rehabilitation engineering and large-scale industrial control systems. Kwang-Hyun Park:received the B.S. M.S. and Ph.D. degrees in electrical engineering and computer science from KAIST Korea in 1994 19997 and 2001 respectively. He is now a researcher at Human-friendly Welfare Robot System Research Center. His research interests include learning control machine learning human-friendly interfaces and service robotics.

It has been found that some huge overshoot in the sense of sup-norm may be observed when typical iterative learning control (ILC) algorithms are applied to LTI systems, even though monotone convergence in the sense of λ-norm is guaranteed. In this paper, a new ILC algorithm with adjustment of learning interval is proposed to resolve such an undesirable phenomenon, and it is shown that the output error can be monotonically converged to zero in the sense of sup-norm when the proposed ILC algorithm is applied. A numerical example is given to show the effectiveness of the proposed algorithm.

关键词： Iterative learning control monotone convergence learning interval sup-norm

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