ICIC2014 Keynote Speakers
August 3-6, 2014
- Lewis Frank L
- Xin Yao
- Vincenzo Piuri
Reinforcement Learning Structures for Real-Time Optimal Control and Differential Games
Lewis Frank L, National Academy of Inventors, Fellow IEEE, InstMC, IFAC
Moncrief-O'Donnell Endowed Chair and Head, Advanced Controls & Sensors Group
UTA Research Institute (UTARI), The University of Texas at Arlington, USA
Personal website: http://www.uta.edu/utari/acs
Abstract: This talk will discuss some new adaptive control structures for learning online the solutions to optimal control problems and multi-player differential games. Techniques from reinforcement learning are used to design a new family of adaptive controllers based on actor-critic mechanisms that converge in real time to optimal control and game theoretic solutions. Continuous-time systems are considered. Application of reinforcement learning to continuous-time (CT) systems has been hampered because the system Hamiltonian contains the full system dynamics. Using our technique known as Integral Reinforcement Learning (IRL), we will develop reinforcement learning methods that do not require knowledge of the system drift dynamics. In the linear quadratic (LQ) case, the new RL adaptive control algorithms learn the solution to the Riccati equation by adaptation along the system motion trajectories. In the case of nonlinear systems with general performance measures, the algorithms learn the (approximate smooth local) solutions of HJ or HJI equations. New algorithms will be presented for solving online the non zero-sum multi-player games for continuous-time systems. We use an adaptive control structure motivated by reinforcement learning policy iteration. Each player maintains two adaptive learning structures, a critic network and an actor network. The result is an adaptive control system that learns based on the interplay of agents in a game, to deliver true online gaming behavior.
Bio-Sketch: F.L. Lewis, Member, National Academy of Inventors. Fellow IEEE, Fellow IFAC, Fellow U.K. Institute of Measurement & Control, PE Texas, U.K. Chartered Engineer. UTA Distinguished Scholar Professor, UTA Distinguished Teaching Professor, and Moncrief-O'Donnell Chair at The University of Texas at Arlington Research Institute. Qian Ren Thousand Talents Professor, Northeastern University, Shenyang, China. IEEE Control Systems Society Distinguished Lecturer. He obtained the Bachelor's Degree in Physics/EE and the MSEE at Rice University, the MS in Aeronautical Engineering from Univ. W. Florida, and the Ph.D. at Ga. Tech. He works in feedback control, reinforcement learning, intelligent systems, and distributed control systems. He is author of 6 U.S. patents, 273 journal papers, 375 conference papers, 15 books, 44 chapters, and 11 journal special issues. He received the Fulbright Research Award, NSF Research Initiation Grant, ASEE Terman Award, Int. Neural Network Soc. Gabor Award 2009, U.K. Inst Measurement & Control Honeywell Field Engineering Medal 2009. Received IEEE Computational Intelligence Society Neural Networks Pioneer Award 2012. Distinguished Foreign Scholar, Nanjing Univ. Science & Technology. Project 111 Professor at Northeastern University, China. Received Outstanding Service Award from Dallas IEEE Section, selected as Engineer of the Year by Ft. Worth IEEE Section. Listed in Ft. Worth Business Press Top 200 Leaders in Manufacturing. Received the 2010 IEEE Region 5 Outstanding Engineering Educator Award and the 2010 UTA Graduate Dean's Excellence in Doctoral Mentoring Award. Elected to UTA Academy of Distinguished Teachers 2012. Texas Regents Outstanding Teaching Award 2013. He served on the NAE Committee on Space Station in 1995.
Recent Ensemble Algorithms for Online and Class Imbalance Learning
Xin Yao, Professor & Ph D, Fellow IEEE, President IEEE CIS
Department of Computer Science, the University of Birmingham, UK
Personal website: http://www.cs.bham.ac.uk/~xin/
Abstract: Previous work on evolving neural networks has focused on single neural networks. However, monolithic neural networks have become too complex to train and evolve for large and complex problems. It is often better to design a collection of simpler neural networks that work collectively and cooperatively to solve a large and complex problem. The key issue here is how to design such a collection, i.e., an ensemble, automatically so that it has the best generalisation ability. This talk first reviews briefly early work on evolving neural networks. Then a previous idea of designing ensembles, negative correlation learning, is explained. Lastly, several recent studies are introduced, which analyze the impact of diversity on online ensemble learning and that on multi-class class imbalance learning. The ideas behind some new ensemble algorithms for online learning, class imbalance learning, and online class imbalance learning will be presented. Applications of such new ensemble learning algorithms will also be mentioned and future research directions discussed.
Bio-Sketch: Xin Yao is a Chair (Professor) of Computer Science and the Director of CERCIA (Centre of Excellence for Research in Computational Intelligence and Applications) at the University of Birmingham, UK. He is an IEEE Fellow and the President (2014-15) of IEEE Computational Intelligence Society (CIS). He won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best paper awards. He won the prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013. He was the Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation and is an Associate Editor or Editorial Member of more than ten other journals. He has been invited to give 70+keynote/plenary speeches at international conferences. His major research interests include evolutionary computation and neural network ensembles.
3D Surface Reconstruction by Using Computational Intelligence Technologies
Vincenzo Piuri, Professor, Ph.D, IEEE Fellow
University degli Studi di Milano, Italy
Personal website: http://homes.di.unimi.it/piuri/
Abstract: Applications based on three-dimensional object models are today very common, and can be found in many fields as design, archeology, medicine, and entertainment. A digital 3D model can be obtained, for example, by means of physical object measurements performed by using a 3D scanner. In this approach, an important step of the 3D model building process consists of creating the object's surface representation from a cloud of noisy points sampled on the object itself. This process can be viewed as the estimation of a function from a finite subset of its points. Problems of this kind occur in many branches of applied mathematics, and computer science. Many techniques have been developed to face them, such as interpolation, extrapolation, regression analysis, and curve fitting. In computational intelligence this problem is viewed as a supervised learning problem, where the two-dimensional vector coordinates of the single point is an input instance, while the third coordinate is considered as an output label. The approximation function identifies how to obtain labels from instances. Several effective computational intelligence paradigms have been developed for solving these kinds of problems. For the solution of the function reconstruction problem, neural techniques, generally, show a good trade-off between computational complexity, accuracy and robustness of the solution with respect to other methods. In this context, there are many different paradigms which are able to find the approximation function, e.g., Multi-layer Perceptron Networks, Radial Basis Function (RBF) Networks, and Support Vector Machines (SVM). In general, there is not a single paradigm better than the others, but each one performs differently depending on the application context. This keynote speech is directed to introduce the needs of the 3D surface reconstruction, to briefly overview the techniques for surface reconstruction, to analyze and discuss in detailed the neural techniques suited for addressing this problem, and to present the most recent results of research.
Bio-Sketch: Vincenzo PIURI has received his Ph.D. in computer engineering at Politecnico di Milano, Italy (1989). He has been Associate Professor at Politecnico di Milano, Italy and Visiting Professor at the University of Texas at Austin and at George Mason University, USA. He is Full Professor in computer engineering (since 2000) and has been Director of the Department of Information Technology at the University degli Studi di Milano, Italy. His main research interests are: signal and image processing, machine learning, pattern analysis and recognition, theory and industrial applications of neural networks, intelligent measurement systems, industrial applications, fault tolerance, cloud computing, internet-of-things, digital processing architectures, embedded systems, arithmetic architectures, and biometrics. Original results have been published in more than 350 papers in international journals, proceedings of international conferences, books, and book chapters.
He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS. He is Editor-in-Chief of the IEEE Systems Journal (2013-15), and has been Associate Editor of the IEEE Transactions on Neural Networks and the IEEE Transactions on Instrumentation and Measurement. He has been IEEE Director and IEEE Delegate for Division X, President of the IEEE Computational Intelligence Society, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, Vice President for Membership of the IEEE Computational Intelligence Society, and Vice President for Education of the IEEE Biometrics Council. He has been elected 2014 IEEE Vice President-elect for Technical Activities. He received the IEEE Instrumentation and Measurement Society Technical Award (2002) for the contributions to the advancement of theory and practice of computational intelligence in measurement systems and industrial applications, the IEEE Instrumentation and Measurement Society Distinguished Service Award (2008), and the IEEE Computational Intelligence Society Meritorious Service Award (2009).