Home Panels at IEEE WCCI 2012

 The IEEE CIS History Panel : Yesterday, Today, and Tomorrow

By Jim Bezdek, U. of  Melbourne and Bernadette Bouchon-Meunier, U. Paris
The participants of this panel will each present no more than 6-8 charts about either the past, the present, or the future of fields and organizations that fall under the CIS umbrella. In particular, we plan to have 2 speakers representing each of the big 3 : fuzzy systems, neural networks, evolutionary computation. Our plan is to have one old codger who can address the history of each topic as it relates to the IEEE CIS or companion societies such as the INNS, IFSA, NAFIPS: and one young turk who can speak to the apparent near/far term future of his or her field from the perspective of an up and coming leader in the field.
 

Computing with Words: Role of Fuzzy, Probability, and Measurement Concepts and Operations

By Boris Kovalerchuk, Central Washington University and Vladik Kreinovich, University of Texas
The goal of this panel is to discuss fundamental concepts that underline a new area of computing with words (CWW) initiated by L. Zadeh. The selection and justification of the techniques based on fuzzy logic, probability theory, or other methodologies is of great importance for the further progress in CWW as well as for other domains that rely on uncertain information such as fuzzy control. Often the key is how to “measure”, quantify the uncertain input in the first place. The representative measurement theory originally developed in mathematical psychology by P. Suppes and others is very relevant to these challenges in CWW. This is a reason why the topic of the discussion is not limited to the “traditional” selection between fuzzy and probabilistic techniques. The most recent relevant discussion started at the Berkeley Initiative on Soft Computing (BISC) group with a “naďve” question from a student: “What is the difference between fuzzy and probability?” that generated multiple answers and a very active discussion. Another set of questions was generated by L. Zadeh asking whether probability can solve a set of CWW tasks that he formulated.

To “warm up” this round table, the WCCI 2012/FUZZ-IEEE 2012 program has scheduled a tutorial “Fuzzy Logic, Probability, and Measurement: Similarities and Differences in Computing with Words” organized by B. Kovalerchuk. This free tutorial will pave the way for the round table. All WCCI participants are invited to this tutorial. The tutorial will help to shorten the introductory part of the round table, and to concentrate on a deeper discussion with more participants. This round table is envisioned as an extension of the round table that B. Bouchon-Meunier organized on uncertainty modeling at the World Conference on Soft Computing in 2011 with invited panelists (Zadeh, Widrow, Kacprzyk, Kovalerchuk, Perlovsky) with multiple requests from the audience to extend it. One of the specific flavors of the panel will be the analysis of different approaches from the multiple perspectives keeping a neutral “denominational” stance. One of such perspectives is the scientific rigor. The optimistic view is that high scientific rigor is compatible with the high uncertainty of CWW. Other perspectives are the time horizon to find the solution of the task, and the importance of the task. Do we have a few minutes or a few years to generate a solution? These constraints impact very significantly the desired and achievable rigor of the solution.

CI Evaluation: What to use and how to encourage its use

The panel will cover the broad range of performance metrics that go beyond the simple calculation of success rate or accuracy on a limited test set. These include sensitivity, specificity, positive and negative predictive value, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC). Particular attention will be paid to the ROC curve formalism and its extension beyond the binary categorization problem. Both parametric and nonparametric uncertainty calculation techniques will be discussed, with particular emphasis on jackknife and bootstrap methods. The importance of good data hygiene in maintaining complete separation of training and test data will be discussed, and the significance of understanding training as well as test-set variability (and human observer variability where appropriate) will be examined.

All of these methodologies have been available for some time, and yet many papers at this and other conferences pay scant attention to any kind of rigorous evaluation of the results they report. The panel will consider how to encourage the better use of these methods in the CI community.

Real world applications of Computational Intelligence

Over the last two decades there has been a growing interest in the need for designing intelligent systems to address complex control and decision making problems. One of the most challenging issues for the intelligent system is to effectively handle real-world uncertainties that cannot be eliminated. The control-related uncertainties include sensor imprecision, instrumentation and process noise and disturbances, unpredictable environmental factors; while the decision-making-related uncertainties include risk analysis, human factors, safety and security evaluation, to name a few.  These uncertainties result in a lack of the full and precise knowledge of the system including its state, dynamics, and interaction with the environment in control applications and a lack of transparency and better understanding of decision analysis in society and policy support. Computationally intelligent systems, based on different techniques, including fuzzy logic, neural networks, genetic algorithms and others, have shown great potential to solve these demanding, real-world problems that exist in uncertain and unpredictable environments.

To be ready for new future applications, we must now consider:

  • Where are we in CI applications?
  • How did we arrive here?
  • Which were the main difficulties we needed to cope with?
  • Which others are still unsolved?
  • What are the promising application fields?
  • Where have we failed?
  • Is there any killer application in CI?
  • Is the future in Cloud-based model development and deployment? 
  • How close are Industry and Academia today?
  • How can we bring them closer?
  • How to manage a successful technology transfer, from prototypes to production-ready models (including model lifecycle)?

Panelists will consider these and other questions, offering to the audience points of view based on their own experience.
 

Preliminary list of panelists

  • Luis Magdalena, European Centre for Soft Computing, Spain (organizer)
  • Piero P. Bonissone, GE Global Research, USA
  • Vincenzo Piuri, Universitá degli Studi di Milano, Italy
  • Yaochu Jin, University of Surrey, UK
  • Kalyanmoy Deb, Indian Institute of Technology Kanpur, India
  • Rami Abielmona, Larus Technologies, Canada
  • Thomas Runkler, Siemens AG, Germany
     

Computational Intelligence in Education and University Curricula

Robert Kozma and Jennie Si (Chairs) 

Haibo He, Janusz Kaczprzyk, Jim Keller, Luis Magdalena, Marios Polycarpou, Lipo Wang

Computational Intelligence is a relatively new research field. A lot of educational materials have been created in various fields of CI in the past decades. However, due to the field's relatively youth, its fundamental achievements has not been organized into a comprehensive curriculum yet. It is crucial for the development of the field to have high-quality educational materials on the state of art of CI. This allows attracting and educating talented and enthusiastic students and documenting the progress in the field. The panel will discuss various areas of CI education, including existing databases and course materials, online resources and video lectures, development of new textbooks, open-source software, and others. Various recommendations for future actions will be discussed as well.

 
Contents by Vinh Quang Bui, DSARC, UNSW@ADFA, Australia / Template by Next Level Design