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IEEE International Symposium on intelligent Control (ISIC)

作者： G.G. Yen P.G. Lima Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

In this paper a hierarchical architecture that combines a high degree of reconfigurability and long-term memory is proposed as a fault tolerant control algorithm for complex nonlinear systems. Dual heuristic programming (DHP) is used for adapting to faults as they occur for the first time in an effort to prevent the build up of a general failure and also as tuning device after switching to a known scenario. A dynamical database, initialized with as much information of the plant as available, oversees the DHP controller. The decisions of which environments to record, when to intervene and where to switch are autonomously taken based on specifically designed quality indices. Results of the application of the complete algorithm to a proof-of-the-concept numerical example help to illustrate the fine interrelations between each of its subsystems.

关键词： Programmable control Adaptive control Control systems Nonlinear control systems Nonlinear systems Stability Databases Switches Robust control Adaptive systems

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Real-time facial patterns mining and emotion tracking 3rd

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3rd International Conference on Advances in Web-Age Information Management, WAIM 2002

作者： Xiang, Zhenggui Yao, Tianxiang Li, Jiang Yu, Keman State Key Laboratory of Intelligent Technology and Systems Dept of Computer Science and Tech Tsinghua University Beijing China Dept of Information System and Electrical Engineering Zhejiang University Zhejiang China Internet Media Group Microsoft Research Asia Beijing China

ISBN: (纸本)9783540440451

In this paper, we present a real-time method for mining the facial patterns and tracking the facial emotion. We first recognize the facial patterns based on a nonsupervised idea of pattern mining from video. To raise the mining speed, we adopt some simple and rapid filters called Gabor-like filters to mine individual feature patterns. To evaluate the facial patterns, we match deformable templates and analyze the histogram of local region. To track the mouth contour, we model dynamically the color distribution of lip and skin by EM algorithm and segment lip color by the Bayes classification rule. Finally, we represent the facial emotion with an avatar by transferring some parameters. Through experiments, our method can rapidly mine the facial patterns and track different facial expressions of different people whether they wear glasses or not. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Template matching

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Entire region filling in indoor environments using neural networks

Entire region filling in indoor environments using neural ne...

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World Congress on intelligent Control and Automation (WCICA)

作者： Chaomin Luo S.X. Yang M. Meng Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Entire region filling is a special type of robot path planning strategy that requires the mobile robot to cover every part of the whole workspace, which has many applications such as cleaning robots, vacuum cleaners, painter robots, land mine detectors, lawn mowers, and window cleaners. In this paper, a novel biologically inspired neural network approach is proposed for entire region filling with obstacle avoidance of a mobile cleaning robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally efficient. It can deal with an unstructured environment with irregular obstacles. The effectiveness of the proposed model is demonstrated by simulation results.

关键词： Filling Indoor environments Neural networks Mobile robots Equations Cleaning Landmine detection Neurons Computational modeling Path planning

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The fuzzy artificial immune system: motivations, basic concepts, and application to clustering and Web profiling

The fuzzy artificial immune system: motivations, basic conce...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： O. Nasaroui F. Gonzalez D. Dasgupta Department of Electrical and Computer Engineering University of Memphis USA Intelligent Security Systems Research Laboratory University of Memphis USA Division of Computer Science University of Memphis USA

The human immune system can be seen as a complex network structure that is able to respond to an almost unlimited multitude of foreign invaders such as viruses and bacteria. Hence, this parallel and distributed adaptive system promises tremendous potential in many intelligent computing applications, including Web mining. Some of these immunity-based techniques involve the development and analysis of algorithms that can identify patterns in observed data in order to make predictions about unseen data. In this paper, we introduce several new enhancements to deal with some of the weaknesses of previous artificial immune system models. In particular, we address the uncertainty and fuzziness inherent in the matching process that takes place between antibodies and antigens. This problem is handled by introducing a fuzzy artificial immune system. A fuzzy artificial immune system mimicking the body's adaptive learning and defense mechanism in the face of invading biological agents is used as a monitoring and learning system for a Web site in the face of all incoming Web requests.

关键词： Fuzzy systems Artificial immune systems Immune system Humans Complex networks Viruses (medical) Microorganisms Adaptive systems Artificial intelligence computer applications

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An extension of weighted strategy sharing in cooperative Q-learning for specialized agents

An extension of weighted strategy sharing in cooperative Q-l...

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International Conference on Neural Information Processing

作者： S.M. Eshgh M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rides for unseen situations. These benefits will be gained if the learning agents know the area of expertise and the expertness values of each other. In this paper, some Q-learning agents with different skills and expertness levels cooperate in learning. The agents use some criteria to judge others information and knowledge. Four expertness criterion, certainty and entropy measures are used to assign degrees of importance to others' Q-Tables. Effects of measuring these values based on their whole Q-Table, a portion of Q-Tables that reflects their proficiencies, and the states in Q-Tables on the learning quality are studied. Simple strategy sharing and two different weighted strategy-sharing methods are used to combine the acquired knowledge from different agents.

关键词： intelligent robots intelligent agent Process control Control systems intelligent control Laboratories intelligent systems Physics Mathematics Entropy

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Cooperative Q-learning with heterogeneity in actions

Cooperative Q-learning with heterogeneity in actions

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IEEE International Conference on systems, Man and Cybernetics

作者： S.M. Reza MirFattah M.N. Ahmadabadi Institute for Studies on Theoretical Physics and Mathematics School of Intelligent Systems Tehran Iran Robotics and AI Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Cooperation in learning improves the speed of convergence and the quality of learning. Special care is needed when heterogeneous agents cooperate in learning. It is discussed that, cooperation in learning may cause the learning process to diverge if heterogeneity is not handled properly. In this paper, it is assumed that two heterogeneous Q-learning agents cooperate to learn. The heterogeneity is assumed in their action order (and not in their action set). A Q-learning-based method is introduced for the agents to learn the mapping among their actions. It is shown that, the agents are able to learn this mapping while cooperating in learning. Some simulation results are reported to show the effectiveness of the proposed method.

关键词： intelligent agent intelligent robots Humans Telecommunication control Artificial intelligence Laboratories intelligent systems Physics Mathematics Convergence

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A new approach to credit assignment in a team of cooperative Q-learning agents

A new approach to credit assignment in a team of cooperative...

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IEEE International Conference on systems, Man and Cybernetics

作者： A. Harati M.N. Ahmadabadi Institute for Studies on Theoretical Physics and Mathematics School of Intelligent Systems Tehran Iran Robotics and AI Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Q-learning is widely used in many multi agent systems. In most cases, a separate critic is considered for qualifying each individual agent behavior or it is assumed that the critic is completely aware of effects of all agents' actions on the team qualification. But, in many cases, the role of each team member in the group performance is not known. In order to distribute a common credit among the agents, a suitable criterion must be provided to estimate the role of each agent in the team performance and to judge if an agent has done a wrong action. In this paper two such criteria, named certainty and expertness, for a team of agents with parallel tasks are introduced. In addition, two methods for reinforcing the agents based on the proposed measures are provided. Some simulation results are also reported to show the effectiveness of the proposed measures and methods.

关键词： intelligent agent Learning systems Multiagent systems intelligent sensors Artificial intelligence Laboratories intelligent systems intelligent robots Physics Mathematics

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Certainty and expertness-based credit assignment for cooperative Q-Learning agents with an AND-type task

Certainty and expertness-based credit assignment for coopera...

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International Conference on Neural Information Processing

作者： A. Harati M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence & AI and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

In multiagent reinforcement learning, inter-agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive. This problem is more critical in groups of independent learners with a joint task. In this research, it is assumed that a critic agent receives the environment feedback and assigns a proper credit to each agent using some measures. Three of such measures for a team of cooperative agents with a parallel and AND-type task are introduced. These measures somehow compare the agents' knowledge. One of these criteria, called normal expertness, is a non-relative measure while two other ones (certainty and relative normal expertness) are relative measure. It is experimentally shown that relative measures work better as they contain more information for the critic agent.

关键词： Feedback intelligent agent intelligent robots Process control Control systems intelligent control Artificial intelligence Robot kinematics Physics computing Reliability engineering

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ROBUST CONGESTION CONTROL IN HIGH SPEED COMMUNICATION NETWORKS: A MODEL PREDICTIVE CONTROL APPROACH

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IFAC Proceedings Volumes 2002年第1期35卷 103-108页

作者： Hua O. Wang Yongru Gu Huajing Fang Laboratory for Intelligent and Nonlinear Control (LINC) Department of Electrical and Computer Engineering Duke University Durham NC 27708-0291 USA Center for Nonlinear and Complex Systems Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

In this paper, the design of explicit rate-based congestion control in high speed communication networks is considered. At a bottleneck node, there are multiple best-effort sources competing with other high priority cross traffic sources. The goal of congestion control is to achieve high link utilization, low packet loss, low delay, and fairness among the best-effort sources. In this paper, the high priority traffic is described by an autoregressive integrated moving average (ARIMA) process. To deal with the propagation delays associated with the best-effort sources, model predictive control, particularly, generalized predictive control, techniques are proposed to solve the congestion problem here. It is demonstrated that the proposed controller performs well and is robust to delay uncertainties. In addition, in a multiple-nodes configuration, the controller provides max-min fairness.

关键词： Communication control applications Model-based control Predictive control Communication networks Time delay Robustness

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An efficient approach of the SOM algorithm to the traveling salesman problem

An efficient approach of the SOM algorithm to the traveling ...

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Brazilian Symposium on Neural Networks

作者： F. Vieira A. Neto J. Costa Computer Engineering and Automation Department Technological Center Intelligent Systems Laboratory Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil Electrical Engineering Department Intelligent Systems Laboratory Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil

This paper presents an approach to the well-known traveling salesman problem (TSP) via competitive neural networks. The neural network model adopted in this work is the Kohonen network or self-organizing maps (SOM), which has important topological information about its neurons configuration. This paper is concerned with the initialization aspects, parameters adaptation, and the complexity analysis of the proposed algorithm. The modified SOM algorithm proposed in this paper has shown better results when compared with others neural network based approaches to the TSP.

关键词： Traveling salesman problems Neurons Neural networks Cities and towns Convergence Joining processes Automation intelligent systems Self organizing feature maps Algorithm design and analysis

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