Keynote Speakers
Professor Mikhail Moshkov
Computer, Electrical, and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Dynamic Programming Approach for Study of Decision Trees
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Professor Andrzej Skowron
Institute of Mathematics, University of Warsaw and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Rough Sets in Interactive Granular Computing
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Professor Louchka PopovaZeugmann
Department of Computer Science, Humboldt University, Berlin, Germany
Time and Concurrency  Three Approaches for Intertwining Time and Petri Nets
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Professor Mikhail Moshkov
Computer, Electrical, and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
 Title
Dynamic Programming Approach for Study of Decision Trees
 Abstract
In the presentation, we consider extensions of dynamic programming approach to the study of decision trees as algorithms for problem solving, as a way for knowledge extraction and representation, and as classifiers which, for a new object given by values of conditional attributes, define a value of the decision attribute. These extensions allow us (i) to describe the set of optimal decision trees, (ii) to count the number of these trees, (iii) to make sequential optimization of decision trees relative to different criteria, (iv) to find the set of Pareto optimal points for two criteria, and (v) to describe relationships between two criteria. The results include the minimization of average depth for decision trees sorting eight elements (this question was open since 1968), improvement of upper bounds on the depth of decision trees for diagnosis of 01faults in readonce combinatorial circuits, existence of totally optimal (with minimum depth and minimum number of nodes) decision trees for monotone Boolean functions with at most six variables, study of timememory tradeoff for decision trees for corner point detection, study of relationships between number and maximum length of decision rules derived from decision trees, study of accuracysize tradeoff for decision trees which allows us to construct enough small and accurate decision trees for knowledge representation, and decision trees that, as classifiers, outperform often decision trees constructed by CART. The end of the presentation is devoted to the introduction to KAUST.

Brief Biography
Mikhail Moshkov is professor in the CEMSE Division at King Abdullah University of Science and Technology, Saudi Arabia since October 1, 2008. He earned master’s degree from Nizhni Novgorod State University, received his doctorate from Saratov State University, and habilitation from Moscow State University. From 1977 to 2004, Dr. Moshkov was with Nizhni Novgorod State University. Since 2003 he worked in Poland in the Institute of Computer Science, University of Silesia, and since 2006 also in the Katowice Institute of Information Technologies. His main areas of research are complexity of algorithms, combinatorial optimization, and machine learning. Dr. Moshkov is author or coauthor of five research monographs published by Springer.
Professor Andrzej Skowron
Institute of Mathematics, University of Warsaw
and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
 Title
Rough Sets in Interactive Granular Computing
 Abstract
Decision support in solving problems related to complex systems requires relevant computation models for the agents as well as methods for incorporating reasoning over computations performed by agents. Agents are performing computations on complex objects (e.g., (behavioral) patterns, classifiers, clusters, structural objects, sets of rules, aggregation operations, (approximate) reasoning schemes etc.). In Granular Computing (GC), all such constructed and/or induced objects are called granules. To model, crucial for the complex systems, interactive computations performed by agents, we extend the existing GC approach to Interactive Granular Computing (IGC) by introducing complex granules (cgranules or granules, for short). Many advanced tasks, concerning complex systems may be classified as control tasks performed by agents aiming at achieving the high quality computational trajectories relative to the considered quality measures over the trajectories. Here, new challenges are to develop strategies to control, predict, and bound the behavior of the system. We propose to investigate these challenges using the IGC framework. The reasoning, which aims at controlling the computational schemes from timetotime, in order to achieve the required targets, is called an adaptive judgement. This reasoning deals with granules and computations over them. Adaptive judgement is more than a mixture of reasoning based on deduction, induction and abduction. Due to the uncertainty the agents generally cannot predict exactly the results of actions (or plans). Moreover, the approximations of the complex vague concepts initiating actions (or plans) are drifting with time. Hence, adaptive strategies for evolving approximations of concepts are needed. In particular, the adaptive judgement is very much needed in the efficiency management of granular computations, carried out by agents, for risk assessment, risk treatment, and cost/benefit analysis. In the lecture, we emphasize the role of the rough set based methods in IGC. The discussed approach is a step towards realization of the Wisdom Technology (WisTech) program, and is developed over years of experiences, based on the work on different reallife projects.

Brief Biography
Andrzej Skowron, ECCA and IRSS Fellow, received the Ph.D. and D.Sci. (habilitation) from the University of Warsaw in Poland. In 1991 he received the Scientific Title of Professor. He is Full Professor in the Faculty of Mathematics, Computer Science and Mechanics at the University of Warsaw and in Systems Research Institute of Polish Academy of Sciences. Andrzej Skowron is the (co)author of more than 400 scientific publications and editor of many books. His areas of expertise include reasoning with incomplete information, approximate reasoning, soft computing methods and applications, rough sets, rough mereology, granular computing, intelligent systems, knowledge discovery and data mining, decision support systems, adaptive and autonomous systems, perception based computing, and interactive computational systems. He was the supervisor of more than 20 PhD Theses. In the period 19952009 he was the EditorinChief of Fundamenta Informaticae journal. He is on Editorial Boards of many international journals. Andrzej Skowron was the President of the International Rough Set Society from 1996 to 2000. He has delivered numerous invited talks at international conferences including a plenary talk at the 16th IFIP World Computer Congress (Beijing, 2000). He was serving as (co)program chair, advisory board member, and PC member of more than 200 international conferences. He was involved in numerous research and commercial projects including dialogbased search engine (Nutech), fraud detection for Bank of America (Nutech), logistic project for General Motors (Nutech), algorithmic trading (Adgam), control of UAV (Linköping University), and medical decision support (PolishAmerican Pediatric Clinic in Cracow). Andrzej Skowron was on the ICI Thomson Reuters list of the mostly cited researchers in Computer Science (globally) in 2012.
Professor Louchka PopovaZeugmann
Department of Computer Science, Humboldt University, Berlin, Germany
 Title
Time and Concurrency  Three Approaches for Intertwining Time and Petri Nets
 Abstract
Time and Petri nets  do they not contradict each other? While time determines the occurrences of events in a system, classic Petri nets consider their causal relationships and represent events as a concurrent system. At first, these two appear to be at odds with each other, but taking a closer look at how time and causality are intertwined, one realizes that time actually enriches Petri nets. There are many possible ways in which time and Petri nets interact, this talk will take a short look at three timedependent Petri nets: Time Petri nets, Timed Petri nets, and Petri nets with timewindows.
For the first nets that we will take a look at, Time Petri nets, enabled transitions may fire only during specified time intervals. The transitions must fire the latest at the end of their intervals if they are still enabled then. At any given moment only one transition may fire. This firing does not take time. For the second class of nets, Timed Petri nets, a maximal set of justenabled transitions fires, and the firing of each transition takes a specific amount of time. The third class of nets, Petri nets with timewindows, portrays time as a minimum and maximum retention for tokens on places. In these nets tokens can be used for firing only during their minimum and maximum retention. At the end of the maximum retention time for a token its time is reset to zero if it was not used for firing. The next period of its retention time on this place then restarts. This repetition can continue indefinitely.
For Time Petri nets, we provide an algorithm which proves the behavioral equivalence of a net where time is designed once with real and once with natural numbers. One can also say that the dense semantics of Time Petri nets can be replaced with discrete semantics. For Timed Petri nets, we introduce two timedependent state equations. These provide a sufficient condition for the nonreachability of states. Last but not least, we prove that Petri nets with timewindows have the ability to realize every transition sequence fired in the net omitting time restrictions. Despite the first experience that time has no influence on the behavior of such nets, we verify that the time can change the liveness behavior of Petri nets with timewindows.
We choose these three classes of timedependent Petri nets to show that time alone does not change the power of a Petri net. In fact, time can or cannot be used to force firing. For Time Petri nets and Timed Petri nets we can say that they are Turingpowerful, and thus more powerful than classic Petri nets. In contrast to these two nets, Petri nets with timewindows have no compulsion to fire. Their expressiveness power is less than that of Turingmachines.

Brief Biography
Louchka PopovaZeugmann studied mathematics and physics and received her M.A. and Ph.D. in mathematics and she wrote her habilitation in computer science at the Humboldt University of Berlin. Between 1979 and 1986 she was employed at the university of economics in Berlin in the field of linear optimization. In 1986 she moved on to work at the Humboldt University of Berlin where she initially was hired as an assistant professor and later as a Privatdozent (researcher/lecturer) and as a visiting professor. Dr. PopovaZeugmann has also worked as a visiting professor at the Universitè ParisEst Crèteil, France. She is currently working as a Privatdozent at the Humboldt University. Her present research interests include design and analysis of timedepended Petri nets and modeling of timesensitive systems. She is the author of 15 journal articles, as well as the book "Time and Petri nets" published by Springer.
