Prof. Dr. Thomas Eiter | October 25, 2019 | 14:00 | S.2.69

Abstract:

The Contextualized Knowledge Repository (CKR) framework was conceived as a logic-based approach for representing context dependent knowledge, which is a well-known area of study in AI, based on description logics. The framework has a two-layer structure with a global context that contains context-independent knowledge and meta-information about the contexts, and a set of local contexts with specific knowledge bases.  In many practical cases, it is desirable that inherited global knowledge can be „overridden“ at the local level. In order to address this need, an extension of CKR with global defeasible axioms was developed: these axioms locally apply to individuals unless an exception for overriding exists; such an exception, however, requires a justification that is provable from the knowledge base.

The formalization of this intuition has some desirable semantic properties, and furthermore allows for a translation of reasoning tasks on extended CKRs to datalog programs under the answer set (i.e., stable) semantics. This work complements other work on nonmonotonic extensions of description logics with an expressive formalism for exception handling by overriding, and adds to the body of results on using deductive database technology in these areas.

This is joint work with Loris Bozzato and Luciano Serafini (Fondazione Bruno Kessler, Trento).

CV:

Thomas Eiter is a full professor in the Faculty of Informatics at Vienna University of Technology (TU Wien), Austria, and Head of the Institute of Information Systems, where he also leads the Knowledge Based Systems Group. From 1996-1998, he was an associate professor of Computer Science at the University of Giessen, Germany.

Prof. Eiter’s current research interests include knowledge representation and reasoning, computational logic, foundations of information systems, and complexity in AI.  He has contributed to the DLV system and some of its extensions, e.g. the DLVHEX system. He has been involved in various national and international research and training projects, and he has been serving on a number of professional committees and boards. Prof. Eiter’s work has been honored with some best paper awards; he is a Fellow of the  European Association for Artificial Intelligence (EurAI), a Member of the Academia Europea, and a Corresponding Member of the Austrian Academy of Sciences.

Prof. Milan Stehlík | October 18, 2019 | 15:00 | V.1.27

Abstract:

Extracting chaotical and stochastic parts of information from time series needs very specific techniques. Motivated by two applications, image processing for cancer discrimination and methane emissions modelling we will explain the necessary techniques for statistical learning on chaotical and stochastic parts from data. In particular, Tsallis Entropy will be introduced and its role in information theory for dynamical system explained. Iterated function systems will be used as an example for chaos re-simulation. Construction of stochastic fractals will be discussed. We will show the importance of decomposition of data to stochastic, deterministic and chaotic part.

CV:

Professor Milan Stehlík  obtained his PhD in 2003 at Comenius University, Bratislava,  Slovakia,  and he habilitated in Statistics in 2011 at Johannes Kepler University in Linz, Austria. During 1.3.2014-1.10.2015 he was Associate Professor at Universidad Técnica Federico Santa María, Chile. In 2015 he received Full Professorship at University of Valparaiso, Valparaiso, Chile.

Currently he is Visiting professor at the Department of Statistics & Actuarial Science, The University of Iowa. In 2018 he was visiting Full Professor at School of Mathematics & Statistical Sciences Arizona State University, AZ, USA. He was involved in several international projects and collaborations in Austria, Spain, Russia, Canada, Germany, USA among others.

He does research in Extremes, Optimal design of experiments, Statistical Modelling, Neural Computing, Cancer discrimination. He servers as Associate Editor for Europe of Neural Computing and Applications, Associate Editor of Journal of Applied Statistics and Revstat.  He has been Principal Investigator of Innovative project LIT-2016-1-SEE-023 Title: Modeling complex dependencies: how to make strategic multicriterial decisions?  at Linz Institute of Technology, Austria and Chilean FONDECYT Regular. He published more than 180 papers and gave more than 190 talks.

Dario Bauso | Friday 6 September | 11:00am | Lakeside Labs B04b B4 1.114

Abstract: The massive penetration of smart technology has changed energy systems and many other engineering applications such as transportation, logistics, and security. In all these contexts, one often observes a large number of machines and humans interacting with each other. Thus there is a need to redefine the boundaries of some engineering disciplines to accommodate both physical and socio-economic components. Control Engineering, the discipline which studies the ways in which one can control the evolution of a system is no exception. A core concept in control is ‘feedback’ whereby a machine (the controller) gets measurements from sensors about the state of the system one wishes to control and sets the inputs to the system. In the presence of numerous machines and humans, one observes the humans turning into strategic players who learn the environment and make decisions knowing that the environment (which is constituted by the other players) is at the same time learning about them and will react accordingly. This takes the concept of feedback to the next level and opens the floor to a number of game theoretic aspects (learning, incentives, pricing).

In this tutorial I will present new perspectives and challenges arising when dealing with co-existing physical and socio-economic components. I will introduce dynamic games with a large number of players, (also known as mean-field games) and discuss recent trends. I will also discuss the role of strategic thinking and learning in competitive scenarios. The tutorial will conclude with a look at bio-inspired collective decision making problems and related evolutionary game models.

Bio: I received the Laurea degree in Aeronautical Engineering in 2000 and the Ph.D. degree in Automatic Control and System Theory in 2004 from the University of Palermo, Italy. Since 2018 I have been with the Jan C. Willems Center for Systems and Control, ENTEG, Faculty of Science and Engineering, University of Groningen (The Netherlands), where I am currently Full Professor and Chair of Operations Research for Engineering Systems. Since 2005 I have also been with the Dipartimento di Ingegneria, University of Palermo (Italy). From 2015 to 2018 I was with the Department of Automatic Control and Systems Engineering, The University of Sheffield (UK), where I was Reader in Control and Systems Engineering. From 2012 to 2014 I was also Research Fellow at the Department of Mathematics, University of Trento (Italy).

I have been academic visitor in several universities. From October 2001 to June 2002, I was a Visiting Scholar at the Mechanical and Aerospace Engineering Department, University of California, Los Angeles (USA). In 2010 I was short-term visiting scholar at the Department of Automatic Control of Lund University (Sweden) and at the Laboratory of Information and Decision Systems of the Massachusetts Institute of Technology (USA). In 2013 I was visiting lecturer at the Department of Engineering Science, University of Oxford (UK) and at the Department of Electrical and Electronic Engineering of Imperial College London (UK). In 2018-2019 I have been guest professor at Keio University, Japan.

My research interests are in the field of Optimization, Optimal and Distributed Control, and Game Theory. Since 2010 I am member of the Conference Editorial Board of the IEEE Control Systems Society. I was Associate Editor of IEEE Transactions on Automatic Control from 2011 to 2016. I am Associate Editor of IFAC Automatica, IEEE Control Systems Letters and Dynamic Games and Applications. I have also been general chair of the 6th Spain, Italy, and Netherlands Meeting on Game Theory (SING 6).