Speaker: Karl Sigmund is a Professor in the Institute of Mathematics, University of Vienna, Austria. His research interest includes Biomathematics, mathematical ecology, chemical kinetics and population genetics; He is interested especially on the field of evolutionary game dynamics and replicator equations. He also works on game dynamical approaches to questions related with the evolution of cooperation in biological and human populations and has written two dozen papers for Nature and Science.

Prof. Karl Sigmund was from 1991 to 2001 managing editor of the Monatshefte für Mathematik, from 1995 to 1997 vice-president and, from 1997 to 2001, president of the Austrian Mathematical Society. He gave many plenary lectures, for instance at the International Congress of Mathematicians 1998, or at three (out of five) European Congresses of Mathematics Applied to Biology and Medicine. In 1996 He became corresponding member and, in 1999, full member of the Austrian Academy of Science. In 2003, he became a member of the Leopoldina. He is (or has been) in the advisory or editorial board of seven scientific journals. He organized many workshops and congresses, for instance the European Science Days in Steyr (2000), the Austrian-German Congress of Mathematicians in Vienna (2001).

Abstract: According to Robert May, President of the Royal Society, the most important unanswered question in evolutionary biology, and more generally in the social sciences, is how cooperative behavior evolved and can be maintained in groups and societies. This talk covers several recent models describing the effects of learning and selection upon the evolution of cooperation.

In particular, the role of sanctions and the role of voluntary participation will be studied both by analytical means and by individual-based computer simulations. Several intriguing results emerge. Institutions which sanction defectors, and thus enforce cooperation, can emerge via self-organization, but only if the agents are given the possibility not to participate in the public goods interaction. Moreover, the role of individual reputation favours the evolution of social behaviour based on (a) contributing to the public goods and (b) punishing those who deviate from the pro-social norm. This seems to offer the possibility of explaining the growth of institutions fostering social behaviour.

A number of recent empirical results, based on anthropological evidence, experimental economics and brain-imaging, is supporting these theoretical findings.

Speaker: Prof. Zbigniew Michalewicz will also give a tutorial at SEAL'06. Click here for his bios.

Abstract: In the modern information era, managers must recognize the competitive opportunities represented by decision-support tools. Adaptive Business Intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address the fundamental questions: What is likely to happen in the future? And what is the best course of action? Adaptive Business Intelligence includes elements of data mining, predictive modeling, forecasting, optimization, and adaptability. The talk introduces the concepts behind Adaptive Business Intelligence, which aims at providing significant cost savings & revenue increases for businesses. A few real-world examples will be shown. Current trends in commercial software, as well as the growing importance of adaptibility, will also be discussed. For more information, see http://www.adaptivebusinessintelligence.com.au.

Speaker: Han La Poutré holds a M.Sc. degree in Mathematics (cum laude) from Eindhoven University of Technology, the Netherlands, and a Ph.D. degree in Computer Science from Utrecht University, the Netherlands; he has been researcher at Princeton University, USA. He received several fellowships and awards, like a NATO Science Fellowship, and a Fellowship of the KNAW (the Royal Netherlands Academy of Sciences and Arts). He is member of the editorial boards of ACM Transactions on Internet Technology, Netnomics, and Computational Management Science.

Speaker: Prof. Gary G. Yen will also give a tutorial at SEAL'06. Click here for his bios.

Abstract: As technology advances and complexity of the systems grows, so does the required degree of system availability. At the same time, faults increase in chance of occurrence, diversity and severity of consequences. In order to achieve the required degrees of reconfiguration and stability, the adaptive controller can benefit greatly if more than the simple instantaneous difference between desired and actual states is available to be used as performance index. Due to the continuous interaction between the controller and the plant, the quality of a certain control strategy can only be fully measured after analyzing all future effects it has on the control mission. This kind of problem is the main focus of approximate dynamic programming, a machine learning technique that solves it through a backward search from the final step. To make the problem tractable to an on-line learning approach, adaptive critic designs (ACDs) introduce a critic block responsible for approximating the cost-to-go or strategic utility function. Such a function is defined by the Hamilton-Jacobi-Bellman equation and represents the core of dynamic programming.

In this talk, we will overview the state-of-the-art in the adaptive critic and its applications in various fields. As an illustrating example, we will apply the ACD to the proposed fault tolerant architecture capable of increasing the availability of complex nonlinear systems potentially subject to a wide range of fault scenarios. Motivated by an encompassing understanding in the areas of fault information extraction and FTC itself, the proposed hierarchical architecture is composed of three levels.

The lowest level is composed of a baseline nonlinear reconfigurable controller that generates identification models and new control solutions for previously unknown faults. The use of ACD grants the architecture the power to preserve system stability and as much performance as possible in the presence of faults that may extend the order or add crucial nonlinearities to the dynamics of the system. Operating on a middle level, a novel supervisor increases the reconfiguration speed of the ACD controller for abrupt faults known at design time as well as faults autonomously modeled and addressed online during a previous occurrence. At the core of the supervisor, two innovative decision logics based on three quality indexes perform fault detection and diagnosis as well as controller malfunction detection. Overviewing the entire architecture, a fault development rule extraction algorithm is positioned at the highest level. Through information gathered from the ACD controller, identifier and from the supervisor, this final component’s goal is to use all historical data from the system to build linguistic rules that inform the human mission planner (e.g., user, operator or pilot) of the probability that different fault scenarios have of taking place at particular future time frames. Once implemented, the fault development rule set will present crucial information to the mission planner when deciding if the desired trajectory of a mission should be altered after the occurrence of a minor fault to reduce the chance of a major, possibly irremediable, fault occurring.

Speaker: James Kennedy, PhD, is a social psychologist who originated the particle swarm algorithm in 1994. His research in the field has included milestones in understanding particle trajectories and convergence; improvements in the use of topologies to control particle behavior and minimize computational cost; extension of the algorithm to the binary case; development of a human-in-the-loop version of the algorithm; development of "velocity-free" models relying on probability distributions; extension and improvement of testing methods for optimization algorithms; and investigations into the evolution of language in particle swarm systems.

Abstract: Recent psychological research finds that Western Europeans and Americans tend to emphasize the individual, relatively neglecting the contexts of behavior, while East Asians tend to view situations holistically, with behavior seen as dependent on its situation. It is not surprising, then, that Western cognitive scientists tended to develop models containing implicit assumptions about the individual isolated from his social context. The particle swarm algorithm employs a social-psychological metaphor of thought as an interindividual phenomenon; learning and problem-solving emerge from the interactions of extremely simple (but sociable) individuals. The conceptualization of thought, knowledge, and intelligence as social patterns provides many insights for computational intelligence research. These insights will be investigated through decomposition and reconstruction of the particle swarm algorithm. The past, present, and future of particle swarm research will provide a framework for understanding human behavior, as well as suggesting new methods for improving the performance of the algorithm.