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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud Computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Towards an ontology for autonomous robots

Towards an ontology for autonomous robots

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25th IEEE/RSJ International Conference on robotics and Intelligent systems, IROS 2012

作者： Paull, Liam Severac, Gaetan Raffo, Guilherme V. Angel, Julian Mauricio Boley, Harold Durst, Phillip J. Gray, Wendell Habib, Maki Nguyen, Bao Ragavan, S. Veera Saeedi G., Sajad Sanz, Ricardo Seto, Mae Stefanovski, Aleksandar Trentini, Michael Li, Howard COBRA Department of Electrical and Computer Engineering University of New Brunswick Fredericton NB E3B 5A3 Canada ONERA-University of Toulouse France Department of Automation and Systems Federal University of Santa Catarina Brazil Department of Computer Engineering Pontificia Universidad Javeriana Bogot Colombia National Research Council Canada US Army ERDC 3909 Halls Ferry Road Vicksburg MS 39180 United States Mechanical Engineering Department School of Sciences and Engineering American University in Cairo New Cairo Egypt Defence Research and Development Canada-CORA Ottawa ON Canada Monash University Sunway Campus Australia Systems Engineering and Automatic Control Autonomous Systems Laboratory Research Group Universidad Politcnica de Madrid Madrid Spain Defence Research and Development Canada-Atlantic Dartmouth NS Canada Department of Computer Science George Washington University United States Defence R and D Canada-Suffield PO Box 4000 Station Main Medicine Hat AB T1A 8K6 Canada

ISBN: (纸本)9781467317375

The IEEE RAS Ontologies for robotics and automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Autonomous Robots sub-group is tasked with developing ontology modules for autonomous robots. This paper describes the work in progress on the development of ontologies for autonomous systems. For autonomous systems, the focus is on the cooperation, coordination, and communication of multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs). The ontologies serve as a framework for working out concepts of employment with multiple vehicles for a variety of operational scenarios with emphasis on collaborative and cooperative missions. © 2012 IEEE.

关键词： Ontology

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On Consensus and Exponentially Fast Social Learning

On Consensus and Exponentially Fast Social Learning

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American Control Conference

作者： Pooya Molavi Kamiar Rahnama Rad Alireza Tahbaz-Salehi Ali Jadbabaie Department of Electrical and Systems Engineering and General Robotics Automation Sensing and Perception (GRASP) Laboratory University of Pennsylvania Philadelphia PA 19104-6228 USA Department of Statistics Columbia University New York NY 10027 USA Decision Risk & Operations Division Columbia Business School Columbia University New York NY 10027 USA

ISBN: (纸本)9781457710957

We analyze a model of social learning in which agents desire to identify an unknown state of the world using both their private observations and information they obtain when communicating with agents in their social neighborhood. Every agent holds a belief that represents her opinion on how likely it is for each of several possible states to be the true one. At each time period, agents receive private signals, and also observe the beliefs of their neighbors in a social network. They then update their beliefs by integrating the information available to them in a boundedly rational fashion. We show that in spite of agents' making new private observations perpetually and the myopic and local updating rule employed by them, agents will eventually reach consensus in their beliefs. This is proved by first showing that agents' beliefs over any state whose truth is inconsistent with their collective observations go to zero exponentially fast.

关键词： Bayesian methods Vectors Social network services Probability distribution Convergence Silicon Educational institutions

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Doubly selective channel estimation for amplify-and-forward relay networks

Doubly selective channel estimation for amplify-and-forward ...

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IEEE Conference on Wireless Communications and Networking

作者： Gongpu Wang Feifei Gao Jiaru Lin Chintha Tellambura School of Computer and Information Technology Beijing Jiaotong University China National Mobile Communications Research Laboratory South East University Nanjing China State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing China Key Laboratory of Universal Wireless Communications Ministry of Education China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

In this paper, the estimation of doubly selective channel is considered for amplify-and-forward (AF) relay networks. The complex exponential basis expansion model (CE-BEM) is chosen to describe the time-varying channel, from which the infinite channel parameters are mapped onto finite ones. Since direct estimation of these coefficients encounters high computational complexity and large spectral cost, we develop an efficient estimator targeting at some specially defined channel parameters. The training sequence design that can minimize the channel estimation mean-square error is also proposed.

关键词： Training Channel estimation Relays Wireless communication Educational institutions Estimation Vectors

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Fuzzy cognitive maps: Basic theories and their applications in medical problems

Fuzzy cognitive maps: Basic theories and their applications ...

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Mediterranean Conference on Control and automation

作者： Groumpos, Peter P. Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras 26500 Rion Patras Greece

ISBN: (纸本)9781457701252

This paper investigates a hybrid methodology that combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for modeling and controlling Supervisory Control systems. A mathematical description of Fuzzy Cognitive Maps (FCM) will be presented and new construction methods will be extensively examined. Synthesizing different FCM will be presented and explained with examples. The neural network nature of FCM will be briefly discussed. The problem of decision making process and a Decision Tree-FCM model is presented. A successful application of FCM theory in a medical problem (bladder tumor) is provided. © 2011 IEEE.

关键词： Fuzzy Cognitive Maps

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Developing Objective Sensitivity Analysis of Periodic systems: Case Studies of Biological Oscillators

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Acta Automatica Sinica 2012年第7期38卷 1065-1073页

作者： Bao-Yun LU Hong YUE Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100190 P.R. China College of Computer Science and Technology Henan Polytechnic University Jiaozuo 454000 P.R. China Department of Electronic and Electrical Engineering University of Strathclyde Glasgow G1 1QE UK

Abstract Sensitivity analysis is a powerful tool in investigating the impact of parameter variations on the change of system behaviours quantitatively. For a periodic system, sensitivity analysis is a challenging problem since the standard sensitivity metrics grow unbounded when time tends to infinity. Objective sensitivity analyses using various oscillation features such as period, phase, amplitude, etc. are therefore needed to circumvent this problem. In this work, a new concept of basal state sensitivity is proposed based on which a phase sensitivity calculation is derived. The improved period sensitivity calculation following an existing algorithm using singular value decomposition (SVD) is also presented, which provides a simple calculation for the basal state sensitivity. These new sensitivity calculations are developed with the purpose to analyse biological oscillators since there is an increasing interest in understanding how oscillations occur and what the main controlling factors are following a growing experimental and computational evidence of oscillations in biological systems. The improved calculation of period sensitivity is shown to be consistent with the previous methods through a well studied circadian rhythm model. The calculation of new objective sensitivities are also testified by the same circadian rhythm model as well as an oscillatory signal transduction pathway model, which further illustrates the efficiency of this approach in handling complex biological oscillators in the presence of reaction conservations.

关键词： Sensitivity analysis periodic systems phase sensitivity period sensitivity biological oscillator

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{\cal U}Boost: Boosting with the Universum

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2012年第4期34卷 825-832页

作者： Chunhua Shen Peng Wang Fumin Shen Hanzi Wang Australian Center for Visual Technologies and School of Computer Science University of Adelaide North Terrace SA Australia School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science and Technology Nanjing University of Science and Technology of PLA Nanjing China Cognitive Science Department School of Information Science and Technology and Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen Fujian China School of Computer Science University of Adelaide SA Australia

It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we design a novel boosting algorithm that takes advantage of the available Universum data, hence the name UBoost. UBoost is a boosting implementation of Vapnik's alternative capacity concept to the large margin approach. In addition to the standard regularization term, UBoost also controls the learned model's capacity by maximizing the number of observed contradictions. Our experiments demonstrate that UBoost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone.

关键词： Boosting Training Educational institutions Optimization Algorithm design and analysis Histograms Accuracy

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Cortex Inspired Model for Inverse Kinematics Computation for a Humanoid Robotic Finger

Cortex Inspired Model for Inverse Kinematics Computation for...

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

作者： Rodolphe J. Gentili Hyuk Oh Javier Molina James A. Reggia José L. Contreras- Vidal Department of Kinesiology School of Public Health Maryland Robotics Center Neuroscience and Cognitive Science Program University of Maryland College Park MD 20742 USA Neuroscience and Cognitive Science Program University of Maryland College Park MD 20742 USA Department of Systems Engineering and Automation Technical University of Cartagena 30202 Cartagena Spain Department of Computer Science University of Maryland Institute for Advanced Computer Studies University of Maryland College Park MD 20742 USA Department of Electrical and Computer Engineering University of Houston Houston TX 77004 USA

ISBN: (纸本)9781424441198

In order to approach human hand performance levels, artificial anthropomorphic hands/fingers have increasingly incorporated human biomechanical features. However, the performance of finger reaching movements to visual targets involving the complex kinematics of multijointed, anthropomorphic actuators is a difficult problem. This is because the relationship between sensory and motor coordinates is highly nonlinear, and also often includes mechanical coupling of the two last joints. Recently, we developed a cortical model that learns the inverse kinematics of a simulated anthropomorphic finger. Here, we expand this previous work by assessing if this cortical model is able to learn the inverse kinematics for an actual anthropomorphic humanoid finger having its two last joints coupled and controlled by pneumatic muscles. The findings revealed that single 3D reaching movements, as well as more complex patterns of motion of the humanoid finger, were accurately and robustly performed by this cortical model while producing kinematics comparable to those of humans. This work contributes to the development of a bioinspired controller providing adaptive, robust and flexible control of dexterous robotic and prosthetic hands.

关键词： reaching prosthetic providing

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Editorial-neural networks and learning systems come together

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IEEE Transactions on Neural Networks and Learning systems 2012年第1期23卷 1-6页

作者： Baesens, Bart Bouboulis, Pantelis Cruces, Sergio Domeniconi, Carlotta Ikeda, Shiro Li, Xuelong Melin, Patricia Rao, V. Sree Hari Schuller, Björn W. Shen, Yi Tang, Huajin Wang, Cong Yang, Jian Zhao, Dongbin Liu, Derong Catholic University of Leuven Belgium University of Athens Greece University of Seville Spain George Mason University United States Institute of Statistical Mathematics Japan University of London United Kingdom Tijuana Institute of Technology Mexico Jawaharlal Nehru Technological University India Technische Universität München Germany Huazhong University of Science and Technology China Institute for Infocomm Research Singapore Singapore South China University of Technology China Nanjing University of Science and Technology China Chinese Academy of Sciences China State Key Laboratory of Management Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Electrical Computer Engineering University of Illinois Chicago IL 60607 United States

This issue marks the beginning of the IEEE Transactions on Neural Networks and Learning systems (TNNLS). By adding «Learning systems» to the title, we now state explicitly the scope of the TRANSACTIONS to include neural networks as well as related learning systems. This issue marks a new era in the history of our TRANSACTIONS. The TRANSACTIONS is now ready to face the challenges of the next 10-20 years. With the evolution of the fields of neural networks in particular and computational intelligence in general, the IEEE Transactions on Neural Networks and Learning systems will continue to grow and to succeed in this ever-changing world. Also included are a few comments about the review process of TNN manuscripts and the introduction of 14 new TNNLS Associate Editors. © 2012 IEEE.

关键词： Learning systems

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Integrated Approach to Hypersonic Vehicle Modeling

Integrated Approach to Hypersonic Vehicle Modeling

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Chinese Control Conference

作者： Xiaofeng Su Yingmin Jia Junping Du Fashan Yu The Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Key Laboratory of Mathematics Informatics and Behavioral Semantics (LMIB) Ministry of Education SMSS Beihang University (BUAA) Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications School of Electrical Engineering and Automation Henan Polytechnic University

ISBN: (纸本)9781467325813

This paper is devoted to the modeling problem of hypersonic vehicle with significant couplings. An integrated approach is developed to analytically model the dynamics of hypersonic vehicle, in which the effects of variable mass, spherical rotating earth, and couplings among the sub-models are considered. Furthermore, a preliminary analysis is conducted using the general data of a hypersonic vehicle. The simulation results show that the maximum of the Coriolis force can reach values up to 7.02% of the vehicle weight, and that the thermal load determined by the nonuniform aerodynamic heating and the properties of the structure has a significant influence on the elastic deformation.

关键词： Hypersonic vehicle modeling Inertial dynamics Aerodynamics Elasticity Heating hypersonic vehicles Aerodynamics elasticity Coriolis force Vehicle weight Heating Couplings Thermal loading elastic deformation

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