Keynote: Managing Granular Information in the Design and Analysis of Human-Centric Fuzzy Systems for Social Computing

Witold Pedrycz

Department of Electrical & Computer Engineering University of Alberta, Edmonton Canada

Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland


About the keynote speaker

    Witold Pedrycz Witold Pedrycz (M�8, SM�0, F�9) is a Professor and Canada Research Chair (CRC - Computational Intelligence) in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. He also holds an appointment of special professorship in the School of Computer Science, University of Nottingham, UK. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. He main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 14 research monographs covering various aspects of Computational Intelligence and Software Engineering. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. Dr. Pedrycz is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences and Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics - part A. He currently serves as an Associate Editor of IEEE Transactions on Fuzzy Systems and a number of other international journals. He has edited a number of volumes; the most recent one is entitled “Handbook of Granular Computing�(J. Wiley, 2008). In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Council. He is a recipient of the IEEE Canada Computer Engineering Medal 2008. In 2009 he has received a Cajastur Prize for Soft Computing from the European Centre for Soft Computing for “pioneering and multifaceted contributions to Granular Computing�


Abstract

Human centricity contributes to the fabric of social computing. Information granularity arises as a fundamental concept facilitating human-system interaction and collaboration among subsystems forming an overall topology of socially oriented systems.

In this talk, we elaborate on the fundamentals of information granularity and introduce a concept of collaborative granular computing. In social computing, we are faced with a number of separate streams of information granules generated by individual sources (users, models) and the resulting individual sources of knowledge formed on their basis. An ultimate objective is to realize an effective interaction at the global basis by invoking some mechanisms of knowledge sharing and collaboration. In this way, each source of knowledge is developed not only by relying on some experimental evidence that becomes locally available but it is exposed to some general conceptual perspective by effectively communicating with other entities and sharing and reconciling revealed local sources of knowledge.

Several fundamental modes of collaboration (which vary with respect to the assumed levels of interaction) are investigated along with the concepts of collaboration mechanisms subsequently leading to the effective way of knowledge sharing and reconciling or calibrating the individual perspectives (points of view).

For illustrative purposes, the underlying architecture investigated in this talk is concerned with rule-based topologies with Ri being a certain information granule (for instance, fuzzy set) formed in the input space and fi denoting any local model realizing a certain mapping confined to the local region of the input space and specified by Ri.

It is also shown that the collaboration and reconciliation of locally available pieces of knowledge ultimately give rise to the concept of higher type information granules, especially fuzzy sets of type-2 and interval-valued fuzzy sets. With this regard, it is shown that the principle of justifiable granularity offers a constructive way of forming type-2 fuzzy sets.


Keynote: Advances in Evolutionary Multi-objective Optimization

Kay Chen Tan

Department of Electrical & Computer Engineering, National University of Singapore


About the keynote speaker

    Kay Chen Tan Associate Professor Kay Chen Tan received the B. Eng degree with First Class Honors in Electronics and Electrical Engineering, and the Ph.D. degree from the University of Glasgow, Scotland, in 1994 and 1997, respectively. He is actively pursuing research in computational and artificial intelligence, with applications to multi-objective optimization, scheduling, automation, data mining, and games.

    Dr Tan has published over 90 journal papers, over 100 papers in conference proceedings, co-authored 5 books including Multiobjective Evolutionary Algorithms and Applications (Springer-Verlag, 2005), Modern Industrial Automation Software Design (John Wiley, 2006; Chinese Edition, 2008), Evolutionary Robotics: From Algorithms to Implementations (World Scientific, 2006; Review), Neural Networks: Computational Models and Applications (Springer-Verlag, 2007), and Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms (Springer-Verlag, 2009), co-edited 4 books including Recent Advances in Simulated Evolution and Learning (World Scientific, 2004), Evolutionary Scheduling (Springer-Verlag, 2007), Multiobjective Memetic Algorithms (Springer-Verlag, 2009), and Design and Control of Intelligent Robotic Systems (Springer-Verlag, 2009).

    Dr Tan has been invited to be a keynote/invited speaker for over 15 international conferences. He served in the international program committee for over 100 conferences and involved in the organizing committee for over 20 international conferences, including the General Co-Chair for IEEE Congress on Evolutionary Computation 2007 in Singapore and the General Co-Chair for IEEE Symposium on Computational Intelligence in Scheduling 2009 in Tennessee, USA. Dr Tan is currently a member of Evolutionary Computation Technical Committee in the IEEE Computational Intelligence Society.

    Dr Tan is currently the Editor-in-Chief of IEEE Computational Intelligence Magazine (CIM). He also serves as an Associate Editor / Editorial Board member of 15 international journals, such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Computational Intelligence and AI in Games, Evolutionary Computation (MIT Press), European Journal of Operational Research, Journal of Scheduling, and International Journal of Systems Science.

    Dr Tan received the Recognition Award (2008) from the International Network for Engineering Education & Research (iNEER) for his outstanding contributions to engineering education and research. He was also a winner of the NUS Outstanding Educator Awards (2004), the Engineering Educator Awards (2002, 2003, 2005), the Annual Teaching Excellence Awards (2002, 2003, 2004, 2005, 2006), and the Honour Roll Awards (2007). Dr Tan is currently a Fellow of the NUS Teaching Academic.


Abstract

Multi-objective evolutionary algorithms are a class of stochastic optimization techniques that simulate biological evolution to solve problems with multiple (and often conflicting) objectives. Advances made in the field of evolutionary multi-objective optimization (EMO) are the results of more than two decades worth of intense research, studying various topics that are unique to MO problems, such as fitness assignment, diversity preservation, balance between exploration and exploitation, elitism and archiving. However many of these studies assume that the problem is deterministic, while the EMO performance generally deteriorates in the presence of uncertainties. In certain situations, the solutions found may not even be implementable in practice. In this talk, challenges faced in EMO research will be discussed and various EMO features and algorithms will be presented. Specifically, the impact of noise uncertainties on MO optimization will be described and the approaches/modifications to basic algorithmic design for robust optimization will be presented. The talk will also discuss the application of EMO techniques for solving engineering problems, such as system design and scheduling, which often involve different competing specifications in a large and highly constrained search space.


Keynote: From Computational Experimentation to Social and Economic Computing

Fei-Yue Wang

Chinese Academy of Sciences, China


About the keynote speaker

    Fei-Yue Wang Fei-Yue Wang received his Ph.D. in Computer and Systems Engineering, minor in Computer Science, from the Rensselaer Polytechnic Institute (RPI), Troy, New York, USA, in 1990. He joined the University of Arizona in 1990 and is the Professor of Systems and Industrial Engineering and Director of the Program for Advanced Research in Complex Systems. In 1998, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences, Beijing, China. Since 2002, he has been the Director of the Key Laboratory of Complex Systems and Intelligence Science at the Chinese Academy of Sciences. His current research interests include intelligent control systems; social computing; modeling, analysis, and control mechanism of complex systems. He has published more than 200 books, book chapters, and papers in those areas since 1984 and received research funding from NSF, DOE, DOT, NNSF, CAS, MOST, Caterpillar, IBM, HP, AT&T, GM, BHP, RVSI, ABB, and Kelon. He is an elected Fellow of the Institute of Electrical and Electronics Engineers (IEEE), International Council of Systems Engineering (INCOSE), International Federation of Automatic Control (IFAC) and the American Association for the Advancement of Science (AAAS).


Abstract

Computational modeling based on heterogeneous individual behavior and complex interactions among individuals or actors is playing an increasingly important role in management sciences and social sciences.In the past, theory building in these disciplines has been largely independent of micro-level observations due to the data availability challenge. As the Web and mobile technologies have been rapidly adopted in all walks of life, rich sets of data are becoming increasingly available at low costs in unprecedentedly detailed level of granularity and resolution. Yesterday's black-boxes are becoming today's white-boxes. Economic and social theories that used to rely primarily on hypotheses such as "invisible hands" are now being re-examined with data and observations, just like their counterparts in natural sciences. In this transition of historic proportion, economic and social computing research is emerging and has drawn attention from multiple research fields. In this talk, we discuss economic and social computing from two perspectives: economic and social computing as a new methodological framework for management and social science research, economic and social computing as a new phenomenon warranting multidisciplinary investigations.