The next, 17th ICAISC will take place in Zakopane in June 3-7, 2018,

Committee on Informatics of the Polish Academy of Sciences

IEEE Poland Section Computational Intelligence Society Chapter

Invited Talks


"Model-free Fault Diagnosis in Sensor Networks"
Cesare Alippi
Politecnico di Milano, Italy and Università della Svizzera Italiana, Switzerland

CESARE ALIPPI received the degree in electronic engineering cum laude in 1990 and the PhD in 1995 from Politecnico di Milano, Italy. Currently, he is a Full Professor of information processing systems with the Politecnico di Milano. He has been a visiting researcher at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC), USI(CH), A*STAR (SIN).
Alippi is an IEEE Fellow, Vice-President education of the IEEE Computational Intelligence Society, member of the Board of Governors of the International Neural Networks Society, Associate editor (AE) of the IEEE Computational Intelligence Magazine, past AE of the IEEE-Trans Instrumentation and Measurements, IEEE-Trans. Neural Networks, and member and chair of other IEEE committees.
In 2016 he received the INNS Gabor award and the IEEE Transactions on Neural Networks and Learning Systems outstanding paper award; in 2004 the IEEE Instrumentation and Measurement Society Young Engineer Award; in 2011 has been awarded Knight of the Order of Merit of the Italian Republic; in 2013 he received the IBM Faculty Award.
Among the others, Alippi was General chair of the International Joint Conference on Neural Networks (IJCNN) in 2012, Program chair in 2014, Co-Chair in 2011 and General chair of the IEEE Symposium Series on Computational Intelligence 2014.
Current research activity addresses adaptation and learning in non-stationary environments and Intelligent embedded systems.
Alippi holds 5 patents, has published in 2014 a monograph with Springer on “Intelligence for embedded systems” and (co)-authored about 200 papers in international journals and conference proceedings.
Home Page:

Availability and usability of data coming from a process/environment, e.g., those generated by a sensor network, introduce serious issues about their quality. In fact, not rarely acquired measurements are affected by sensor aging and faults which might introduce errors impacting on the correctness of the subsequent decision making process. The ability to detect faults is a mandatory step, which cannot be underestimated or neglected in real deployments.
In this direction, Fault Diagnosis Systems (FDS) are tools designed to supervise a process operation in order to detect, isolate and identify potential faults and, possibly, design accommodation actions. However, most FDS assume that some of -not necessarily amenable- hypothesis are satisfied, e.g., a description for the process is available; the system model is linear; a fault dictionary containing the fault signatures is provided; the nature of the fault profile and its development are known.
Current research in machine learning aims at removing/weakening the above assumptions so that FDS can be designed directly from available data, possibly within a cognitive framework.
The talk will focus on aspects related to the design of cognitive FDSs for sensor networks able to discriminate between faults, changes in the environment and model bias within an evolving framework.


"Internet of Things (IoT) Analytics"
Albert Bifet
Telecom ParisTech, France

Albert Bifet is Associate Professor at Telecom ParisTech and Honorary Research Associate at the WEKA Machine Learning Group at University of Waikato. Previously he worked at Huawei Noah's Ark Lab in Hong Kong, Yahoo Labs in Barcelona, University of Waikato and UPC BarcelonaTech. He is the author of a book on Adaptive Stream Mining and Pattern Learning and Mining from Evolving Data Streams. He is one of the leaders of MOA and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He is serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of BigMine (2015, 2014, 2013, 2012), and ACM SAC Data Streams Track (2016, 2015, 2014, 2013, 2012)
Home Page:

Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in stream mining. In this talk, I will present an overview of data stream mining, and I will introduce some popular open source tools for data stream mining.


"Mapping psychological concepts on higher order brain dynamics"
Włodzisław Duch (Web Page)
Department of Informatics, and NeuroCognitive Laboratory, Center for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Poland

Wlodzislaw Duch heads the Neurocognitive Laboratory in the Center of Modern Interdisciplinary Technologies, and the Department of Informatics, both at Nicolaus Copernicus University, Torun, Poland. In 2014-15 he has served as a deputy minister for science and higher education in Poland, and in 2011-14 as the Vice-President for Research and ICT Infrastructure at his University. Before that he has worked as the Nanyang Visiting Professor (2010-12) in the School of Computer Engineering, Nanyang Technological University, Singapore where he also worked in 2003-07. MSc (1977) in theoretical physics, Ph.D. in quantum chemistry (1980), postdoc at Univ. of Southern California, Los Angeles (1980-82), D.Sc. in applied math (1987); worked at the University of Florida; Max-Planck-Institute, Munich, Germany, Kyushu Institute of Technology, Meiji and Rikkyo University in Japan, and several other institutions. He is/was on the editorial board of IEEE TNN, CPC, NIP-LR, Journal of Mind and Behavior, and 14 other journals; was co-founder & scientific editor of the “Polish Cognitive Science” journal; for two terms has served as the President of the European Neural Networks Society executive committee (2006-2008-2011), is an active member of IEEE CIS Technical committee; International Neural Network Society Board of Governors elected him to their most prestigious College of Fellows. He works as an expert of the European Union science programs; published over 300 scientific and over 200 popular articles on diverse subjects, has written or co-authored 4 books and co-edited 21 books, his DuchSoft company has made GhostMiner software package marketed by Fujitsu company.
Wlodek Duch is well known for development of computational intelligence (CI) methods that facilitate understanding of data, general CI theory based on similarity evaluation and composition of transformations, meta-learning schemes that automatically discover the best model for a given data. He is working on development of neurocognitive informatics, focusing on algorithms inspired by cognitive functions, information flow in the brain, learning and neuroplasticity, understanding of attention, integrating genetic, molecular, neural and behavioral levels to understand attention deficit disorders in autism and other diseases, infant learning and toys that facilitate mental development, creativity, intuition, insight and mental imagery, geometrical theories that allow for visualization of mental events in relation to the underlying neurodynamics. He has also written several papers in the philosophy of mind, and was one of the founders of cognitive sciences in Poland. Since 2014 he is heading a unique NeuroCognitive Laboratory, that involves experts in hardware and software, signal processing, physics, cognitive science, psychology and philosophy. His Lab works with infants, preschool children, students and older people, using neuroimaging techniques, behavioral experiments and computational modelling.
With a wide background in many branches of science and understanding of different cultures he bridges many scientific communities. To find a lot of information about his activity including his full CV just type "W. Duch" in Google.

Describing mental processes we use many psychological concepts, such as mind, consciousness, working memory, attention, thinking, creativity. Can we understand these concepts in terms of the mechanics of brain processes?
Different levels of description of neural processes are needed to address different types of questions, but for understanding psychological concepts models of neural dynamics are the most important. Unfortunately there is little overlap between neuroimaging and computational cognitive neurodynamics communities. Papers analyzing neuroimaging experiments frequently belong to the "modern phrenology" types, discussing only activations of selected brain areas correlated with behavioral tasks, instead of referring to cognitive architectures and theoretical models based on neurodynamics.
A step towards better explanations of neuroimaging results is based on network science that helps to analyze the whole-brain functional network dynamics. I will analyze our recent n-back experiments using hub detection and the Network-Based Statistics, showing how functional brain network dynamics changes during increasing cognitive demands on working memory, how it relates to errors made, refer to the Global Neuronal Workspace (NBS) methods.
If a tasks is simple brain networks effortlessly process information in a highly segregated way, but more complex tasks require engagement of multiple distributed networks, functional integration of many brain areas creating new hubs using long-range connections. The whole network modularity decreases and in specific brain network areas local hubs vanish and some regions join global hubs. This functional reorganization can be viewed as a higher-order dynamics. First there is connectome, anatomical organization of brain connection. Second, there are functional dynamical states that process information in the active subnetworks. Third, there are processes that respond to the changing cognitive load reorganizing functional networks.
Such view leads to many questions that should lead to deeper understanding of brain processes.

1. Finc K, Bonna K, Lewandowska M, Wolak T, Nikadon J, Dreszer J, Duch W, Kühn S. Whole-brain functional network modularity and efficiency changes related to cognitive effort. Human Brain Mapping (in revision)
2. Gravier A, Quek H.C, Duch W, Abdul Wahab, Gravier-Rymaszewska J. Neural network modelling of the influence of channelopathies on reflex visual attention. Computational Neurodynamics 10(1), 49-72, 2016.
3. Duch W, Dobosz K, Visualization for Understanding of Neurodynamical Systems. Cognitive Neurodynamics 5(2), 145-160, 2011.
4. Dobosz K, Duch W, Understanding Neurodynamical Systems via Fuzzy Symbolic Dynamics. Neural Networks 23, 487-496, 2010.


"Evolving Social Networks: understanding the trajectories of communities"
João Gama
LIAAD INESC TEC and FEP University of Porto, Portugal

Bio: João Gama received his Ph.D. in Computer Science in 2000. He is a senior researcher at INESC TEC. He has worked in several National and European projects on Incremental and Adaptive learning systems, Ubiquitous Knowledge Discovery, Learning from Massive, and Structured Data, etc. He served as Program chair at several Machine Learning and Data Mining conferences. He is author of a monography on Knowledge Discovery from Data Streams and more than 200 peer-reviewed papers in areas related to machine learning, data mining, and data streams.

In our digital world, social networks are becoming more and more influential in the way how people interact, communicate, and make decision. Social networks produce huge volumes of data in the form of continuous streams. These streams apart from being massive, rapid and transient, are also evolving in nature. Analyzing and mining such evolutionary streams gives actionable insights into data. In this talk we discuss the research opportunities opened in analysing evolving data, detecting events, identifying communities and tracking the evolution of communities through the events they trigger.


"Towards cognitive socio-economic modeling: a crucial role of human judgments, psychological biases, desires and intentions"
Janusz Kacprzyk
Full Member, Polish Academy of Sciences
Member, Academia Europaea
Member, European Academy of Sciences and Arts
Foreign Member, Bulgarian Academy of Sciences
Foreign Member, Spanish Royal Academy of Economic and Financial Sciences (RACEF)
Systems Research Institute, Polish Academy of Sciences
ul. Newelska 6, 01-447 Warsaw, Poland

Janusz Kacprzyk graduated from Warsaw University of Technology, Poland, with M.Sc. in automatic control and computer science, obtained in 1977 Ph.D. in systems analysis and in 1991 D.Sc. in computer science. He is Professor of Computer Science at the Systems Research Institute, Polish Academy of Sciences, and at WIT – Warsaw School of Information Technology, and Professor of Automatic Control at PIAP – Industrial Institute of Automation and Measurements. He is Honorary Foreign Professor at the Department of Mathematics, Yli Normal University, Xinjiang, China, and Visiting Scientist at RIKEN Brain Research Institute, Tokyo, Japan. He is Full Member of the Polish Academy of Sciences, Member of Academia Eueopaea (Informatics), Member of European Academy of Sciences and Arts (Technical Sciences), Foreign Member of the Spanish Royal Academy of Economic and Financial Sciences (RACEF), and Foreign Member of the Bulgarian Academy of Sciences. He is Fellow of IEEE, IFSA, EurAI (ECCAI) and MICAI.
He has been a frequent visiting professor in the USA, Italy, UK, Mexico, China. He has been a member of evaluation commissions of many foreign universities. His main research interests include the use of modern computation computational and artificial intelligence tools, notably fuzzy logic, in decisions, optimization, control, data analysis and data mining, with applications in databases, ICT, mobile robotics, systems modeling etc. He authored 6 books, (co)edited more than 100 volumes, (co)authored ca. 550 papers, including ca. 80 in journals indexed by the WoS. His bibliographic data are: due to Google Scholar - citations: 19491; h-index: 64, due to Scopus: citations: 5241; h-index: 32; due to WoS: citation: 4212, h-index: 27. He is the editor in chief of 6 book series at Springer, and of 2 journals, and is on the editorial boards of ca. 40 journals. He is a member of the Adcom of IEEE CIS, and was a Distinguished Lecturer of IEEE CIS.
He received many awards: 2006 IEEE CIS Pioneer Award in Fuzzy Systems, 2006 Sixth Kaufmann Prize and Gold Medal for pioneering works on soft computing in economics and management, 2007 Pioneer Award of the Silicon Valley Section of IEEE CIS for contribution in granular computing and computing in words, 2010 Award of the Polish Neural Network Society for exceptional contributions to the Polish computational intelligence community, IFSA 2013 Award for his lifetime achievements in fuzzy systems and service to the fuzzy community, and the 2014 World Automation Congress Lifetime Award for contributions in soft computing, the 2016 Award of the International Neural Network Society – Indian Chapter for Outstanding Contributions to Computational Intelligence. He is President of the Polish Operational and Systems Research Society and Past President of International Fuzzy Systems Association.

This work concerns the problem of socio-economic modeling, more specifically, regional modeling using a multistage planning model over some planning horizon, for instance 5 – 20 years, which is formulated in terms of some finite (a few, usually) scenarios of development which is meant as both investments to be spent and some goals to be attained. The model is represented by a finite state dynamic system in which the state (output) is equated with some life quality indicators, and the input (control) is the amount of some investment and other expenditures which imply changes in the values of life quality indicators. This model was developed in its conceptual form in the late 1970s – early 1980s, during the author’s long time work for various regional development projects at the International Institute for Applied Systems Analysis (IIASA) in Laxenburg, Austria, one for the best known and most prestigious think tank in the broadly perceived systems analysis. Then, it was extended by using various fuzzy tools and techniques to model imprecision, as well as various more traditional probabilistic tools and techniques to model uncertainty, and – recently – extended by adding some nonconventional aggregation operators, affective computing, analysis of desires and intentions, cognitive informatics, etc. Over the decades, the model has been employed for various regional studies all over the world, notably the Upper Noteć in Poland, Tisza in Hungary, Kinki in Japan, The Danube Watershed, etc. The model has been mentioned as one of the best examples of fuzzy modeling in the special volume published for the 50th anniversary of the British Operational Research Society (cf. Thomas L.C., Ed. (1987) Golden Developments in Operational Research, Pergamon Press, New York, and for its fuzzy dynamic programming based algorithm the author has been awarded the 2006 Fuzzy Pioneer Award of the IEEE Computational Intelligence Society. Basically, at each planning stage, the region, presented as a system under control, with the states (outputs) equated with some life quality indicators, is subject to some investments (spending) due to some development scenario, which implies a change of the values of the life quality indicators assumed. These changes occur at each planning stage, over some assumed horizon, and their temporal evolution implies a trajectory of regional development. The basic problem is to find such investments (expenditures) which would imply the best possible evaluation of the trajectory. This evaluation is a very complicated issue because of many sophisticated, both objective and human judgment based, evaluations and/or assessments which can be measured and just perceived, but both are related to some satisfaction of the goodness of development. This is, however, a very complicated problem. First, there are two main actors, the authorities (usually national as regions may be not rich enough) who are planning and responsible for the development, and inhabitants for whom the development is to proceed. Basically, the authorities would rather be more cost conscious while the inhabitants – more effect (better values of life quality indicators) conscious. Both actors would prefer some stability in the sense of a possibly evenly distributes costs and effects over the planning horizon, and also some fair division of funds meant for the improvement of the particular life quality indicators., and a proper aggregation of satisfaction from the values of the life quality indicators attained. Moreover, all evaluations and assessment can be both objective, resulting from the sheer values of attained, and subjective, resulting from an analysis of intentions, desires, etc., and taking into account some psychological biases, notably the so called status quo bias which basically means that the human being prefer smaller changes. An important aspect is a fairness orientation of both the authorities and inhabitants, maybe with more emphasis on the so called outcome-based inequity aversion approach, in the case of authorities, and the intention-based reciprocity approach, in the case of inhabitants. In all these cases a sophisticated fusing of outcomes related to the particular life quality indicators and their temporal distribution, actors, and their specific judgmental characteristics, etc. is to be applied. We use some results of behavioral economics, psychology, intention modeling, affective computing, Wang’s cognitive informatics, etc. Moreover, we show the use some elements of "natural language technology", notably natural language generation in the context of linguistic summarization. Virtually all the above aspects of socio-economic development, and its evaluation/assessment, related to life quality indicators and distributed over the planning horizon, involves clearly both much uncertainty and imprecision. We show mainly the imprecision related aspects, dealt with using fuzzy logic, develop a multistage planning model, and show some examples.


"Spiking Neural Networks: The Machine Learning Approach" (cancelled, moved to ICAISC 2018)
Nikola Kasabov, FIEEE, FRSNZ (Web Page)
Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, New Zealand

Professor Nikola Kasabov is Fellow of IEEE, Fellow of the Royal Society of New Zealand and DVF of the Royal Academy of Engineering, UK. He is the Director of the Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland. He holds a Chair of Knowledge Engineering at the School of Computing and Mathematical Sciences at Auckland University of Technology. Kasabov is a Past President and Governor Board member of the International Neural Network Society (INNS) and also of the Asia Pacific Neural Network Assembly (APNNA). He is a member of several technical committees of IEEE Computational Intelligence Society and a Distinguished Lecturer of the IEEE CIS (2012-2014) He is a Co-Editor-in-Chief of the Springer journal Evolving Systems and has served as Associate Editor of Neural Networks, IEEE TrNN, IEEE TrFS, Information Science, J. Theoretical and Computational Nanosciences, Applied Soft Computing and other journals. Kasabov holds MSc and PhD from the TU Sofia, Bulgaria. His main research interests are in the areas of neural networks, intelligent information systems, soft computing, bioinformatics, neuroinformatics. He has published more than 600 publications that include 15 books, 180 journal papers, 80 book chapters, 28 patents and numerous conference papers. He has extensive academic experience at various academic and research organisations in Europe and Asia, including: TU Sofia, University of Essex, University of Otago, Advisor- Professor at the Shanghai Jiao Tong University, Guest Professor at ETH/University of Zurich. Prof. Kasabov has received the APNNA "Outstanding Achievements Award", the INNS Gabor Award for "Outstanding contributions to engineering applications of neural networks", the EU Marie Curie Fellowship, the Bayer Science Innovation Award, the APNNA Excellent Service Award, the RSNZ Science and Technology Medal, and others. He has supervised to completion 38 PhD students. More information of Prof. Kasabov can be found on the KEDRI web site:

The current development of the third generation of artificial neural networks - the spiking neural networks (SNN) along with the technological development of highly parallel neuromorphic hardware systems of millions of artificial spiking neurons as processing elements, makes it possible to model complex data streams in a more efficient, brain-like way [1,2]. The talk first presents some principles of deep learning implemented in a recently proposed evolving SNN (eSNN) architecture called NeuCube. NeuCube was first proposed for brain data modelling [3,4]. It was further developed as a general purpose SNN development system for the creation and testing of spatio/spectro temporal data machines (STDM) to address challenging data analysis and modelling problems. A version of the development system is available free from:, along with papers and case study data. The talk introduces a methodology for the design and implementation of SNN systems for deep learning, modelling and understanding of spatio-/spectro temporal data, referred here as STDM [5]. A STDM has modules for: preliminary data analysis, data encoding into spike sequences, unsupervised learning of spatio-temporal patterns, classification, regression, prediction, model visualisation and knowledge discovery. The methodology is illustrated on benchmark data with different spatial/temporal characteristics, such as: EEG data for brain computer interfaces; fMRI data classification; personalised and climate date for individual stroke occurrence prediction [6]. The talk discusses implementation on highly parallel neuromorphic hardware platforms such as the Manchester SpiNNaker [7] and the ETH Zurich chip [8,9]. The STDM are not only significantly more accurate and faster than traditional machine learning methods and systems, but they lead to a significantly better understanding of the data and the processes that generated it. A STDM can be used to predict early and accurately events and outcomes through the ability of SNN to be trained to spike early, when only a part of a new pattern is presented as input data. New directions for the development of SNN and STDM are pointed towards a further integration of principles from the science areas of computational intelligence, bioinformatics and neuroinformatics [10,11].

1. EU Marie Curie EvoSpike Project (Kasabov, Indiveri):
2. Schliebs, S., Kasabov, N. (2013). Evolving spiking neural network-a survey. Evolving Systems, 4(2), 87-98.
3. Kasabov, N. (2014) NeuCube: A Spiking Neural Network Architecture for Mapping, Learning and Understanding of Spatio-Temporal Brain Data, Neural Networks, 52, 62-76.
4. Kasabov, N., Dhoble, K., Nuntalid, N., Indiveri, G. (2013). Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. Neural Networks, 41, 188-201.
5. Kasabov, N. et al (2015) A SNN methodology for the design of evolving spatio-temporal data machines, Neural Networks, in print.
6. Kasabov, N., et al. (2014). Evolving Spiking Neural Networks for Personalised Modelling of Spatio-Temporal Data and Early Prediction of Events: A Case Study on Stroke. Neurocomputing, 2014.
7. Furber, S. et al (2012) Overview of the SpiNNaker system architecture, IEEE Trans. Computers, 99.
8. Indiveri, G., Horiuchi, T.K. (2011) Frontiers in neuromorphic engineering, Frontiers in Neuroscience, 5, 2011.
9. Scott, N., N. Kasabov, G. Indiveri (2013) NeuCube Neuromorphic Framework for Spatio-Temporal Brain Data and Its Python Implementation, Proc. ICONIP 2013, Springer LNCS, 8228, pp.78-84.
10. Kasabov, N. (ed) (2014) The Springer Handbook of Bio- and Neuroinformatics, Springer.
11. Kasabov, N (2016) Spiking Neural Networks: The machine Learning Approach, Springer, 2016


"Rapid testing, prototyping and validating new ideas thanks to: Data Lake, Azure Machine Learning and Azure Notebook"
Tomasz Kopacz

During 45 minutes talk we will see how to use cloud based technologies to speed up developing a new ideas and concepts. In short – how to do RAPID prototyping in 2017. For sure, we will focus on:
- Really big data processing on Data Lake.
- Testing machine learning ideas on many large (and small) data sets.
- Develop and discuss Python / R applications using Azure Notebooks.
Be warned: this session will be a little bit technical!


"Decomposable Graphical Models in Industrial Applications: On Learning and Revision"
Rudolf Kruse
Faculty of Computer Science, University of Magdeburg, Germany

Rudolf Kruse is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Fellow of the European Association for Artificial Intelligence (formerly ECCAI), and Fellow of the International Fuzzy Systems Association (IFSA). He obtained his Ph.D. and Habilitation in Mathematics from the Technical University of Braunschweig in 1980 and 1984 respectively. Following a stay at the Fraunhofer Gesellschaft, he joined the Technical University of Braunschweig as a Professor of Computer Science in 1986. Since 1996 he holds the Chair of Computational Intelligence at the Otto-von-Guericke University of Magdeburg. His current research interests include data science and intelligent systems. Rudolf Kruse has coauthored 15 monographs and 25 books as well as more than 400 refereed technical papers in various scientific areas. He has supervised more than 30 Ph.D. students. His group is successful in various industrial applications in cooperation with companies such as Volkswagen, SAP, Daimler, and British Telecom.

Decomposable Graphical Models are of high relevance for complex industrial applications. The Markov network approach is one of their most prominent representatives and an important tool to structure uncertain knowledge about high dimensional domains. But also relational and possibilistic decompositions turn out to be useful to make reasoning in such domains feasible. In this talk we study how to generate the structure of the model from data as well as from background knowledge. A second important task in this context is the efficient revision of the model, when new data and knowledge become available. Here, the problem of handling inconsistencies is of utmost relevance for real world applications. We address these topics by presenting a successful complex application in automotive industry.


"System Modeling and Data Analytics - A Perspective of Information Granules "
Witold Pedrycz
Department of Electrical & Computer Engineering
University of Alberta, Edmonton Canada
Systems Research Institute, Polish Academy of Sciences
Warsaw, Poland

The apparent challenges in system modeling and data analytics inherently associate with large volumes of data, data variability, and an evident quest for transparency and interpretability of established constructs and obtained results. We advocate that information granules play a pivotal role in addressing these key challenges. We demonstrate that a framework of Granular Computing along with a diversity of its formal settings offers a critically needed conceptual and algorithmic environment.

A suitable perspective built with the aid of information granules is advantageous in realizing a suitable level of abstraction and becomes instrumental when forming sound, practical problem-oriented tradeoffs among precision of results, their easiness of interpretation, value, and stability (as lucidly articulated through the principle of incompatibility coined by Zadeh). All those aspects emphasize importance of actionability and interestingness of the produced findings.

Special attention is paid to the construction of information granules and the talk tackles their design issue by emphasizing that the emergence of semantically sound granules has be justified by available experimental evidence. The rationale behind the emergence of information granules of higher type is offered and their unique role in realizing a hierarchy of processing and coping with a distributed nature of available data is presented. In system modeling, information granules are instrumental in the realization of granular models involving information granules of increasingly higher type. With this regard, we introduce concepts of granular spaces, viz. spaces of granular parameters of the models and granular input spaces, which play a pivotal role in granular models.

The detailed investigations are also reported for several selected classes of problems: (i) building granular auto-encoders in architectures of deep learning, (ii) realization of imputation mechanisms augmented by quantification of quality of imputed data, (iii) construction and analysis of hotspots, and (iv) carrying out knowledge transfer.


"Decision under risk and uncertainty as a multi-quantile decision problem"
Roman Slowinski
Institute of Computing Science, Poznań University of Technology and Systems Research Institute, Polish Academy of Sciences, Poland

Roman Slowinski is a Professor and Founding Chair of the Laboratory of Intelligent Decision Support Systems at the Institute of Computing Science, Poznań University of Technology in Poland. Since 2002 he is also Professor at the Systems Research Institute of the Polish Academy of Sciences in Warsaw.
He is a full member of the Polish Academy of Sciences and, presently, elected president of the Poznań Branch of the Academy. He is also a member of Academia Europaea.
In his research, he combines Operations Research and Computational Intelligence. Today Roman Słowiński is renown for his seminal research on using rough sets in decision analysis, and for his original contribution to preference modeling and learning in decision aiding.
He is recipient of the EURO Gold Medal, and Doctor Honoris Causa of Polytechnic Faculty of Mons, University Paris Dauphine, and Technical University of Crete. In 2005 he received the Annual Prize of the Foundation for Polish Science - regarded as the highest scientific honor awarded in Poland. Since 1999, he is principal editor of the European Journal of Operational Research, a premier journal in Operations Research. He is coordinator of the EURO Working Group on Multiple Criteria Decision Aiding, and past president of the International Rough Set Society.

In order to learn Decision Maker's (DM’s) preferences and make robust decisions under risk and uncertainty, we apply Robust Ordinal Regression (ROR) [1]. This technique was originally proposed for multiple criteria decision aiding (MCDA) with the aim of taking into account the whole set of instances of a chosen type of preference model, which are compatible with preference information supplied by the DM in terms of holistic preference comparisons of some alternatives. ROR yields two weak preference relations, necessary and possible, in the whole set of alternatives; the necessary weak preference relation holds if an alternative is at least as good as another one for all instances of the preference model compatible with the DM’s preference information, while the possible weak preference relation holds if an alternative is at least as good as another one for at least one compatible instance. To apply ROR to decision under risk and uncertainty we reformulate this problem in terms of MCDA. This is obtained by replacing an uncertain outcome of a decision problem on a set of alternatives (e.g., a gain on investment) by a set of quantiles of the outcome distribution, which are meaningful for the DM. These quantiles become evaluation criteria of a deterministic MCDA problem, equivalent to the decision problem under risk and uncertainty. To solve the MCDA problem we apply a ROR method, like GRIP or ELECTRE-GKMS, involving utility function or outranking relation as a preference model, respectively. We illustrate our proposal by solving an example of the famous newsvendor problem. The presentation is based on our recent publication [2].

[1] S. Corrente, S. Greco, M. Kadziński, R. Słowiński: Robust ordinal regression in preference learning and ranking. Machine Learning, 93 (2013) 381-422.
[2] S. Corrente, S. Greco, B. Matarazzo, R. Słowiński: Robust ordinal regression for decision under risk and uncertainty. Journal of Business Economics, 86 (2016) no.1, 55-83.


Ryszard Tadeusiewicz
AGH University of Science and Technology, Krakow, Poland

Ryszard Tadeusiewicz obtained his Master of Science degree with honors from the AGH University of Science and Technology in 1971 and started research in the areas of bio-cybernetics, control engineering, and computer science. In 1975 he was awarded by the Ph.D. degree, and in 1981 the degree of Doctor of Sciences. In 1986 he was appointed as associate professor and in 1991 full professor at the AGH University of Science and Technology. He has written over 800 scientific papers, published in prestigious Polish and foreign scientific journals, as well as in numerous conference proceedings - both national and international. Professor Tadeusiewicz has also written over 80 scientific monographs and books, among them several highly popular textbooks, which were adopted by dozens of Polish universities and had many editions. Prof. Tadeusiewicz supervised the total of 69 PhD students as the primary advisor at the AGH University of Science and Technology, Academy of Economics, and Collegium Medicum. In March 2002, Professor Tadeusiewicz was elected as Corresponding Member of the Polish Academy of Sciences (PAN) and in May 2012 he was elected as Full Member of PAN. He was three times elected as President of Cracow branch of PAN. In 1996, he was elected as Deputy Rector for Science of the AGH University of Science and Technology, and in January 1998 Rector of that University. He was re-elected again as Rector in 1999 and once more in May 2002 for the period 2002-2005. This makes him the longest-serving Rector of the AGH University of Science and Technology in Cracow. Details and most up-to-date information are available at the website

Biomedical engineering is new and fast developed area of scientific research and also very important element of up-to-date technology. Development of science and technology, which can serve doctors as a weapon against illnesses and death is especially worthy of support area of intellectual and technological activity. Seeking to bridge the gap between engineering and medicine, what is the main goal of biomedical engineering, we can use computational intelligence as very important tool for advance health care treatment, including diagnosis, monitoring, and therapy. Taking into account current state of art as well own research, author will present some illustrative examples of computational intelligence applications for more effective medical data analysis (for enhanced diagnosis). Moreover, interesting and inspiring examples of computational intelligence applications for therapy enhancement will be presented along with discussion of specific problems connected with communications between medical doctors and artificial (computer based) advisors. A very interesting part of computational intelligence used in biomedical engineering is development of intelligent medical devices. Some examples, including intelligent pacemakers, infusion pumps, the heart-lung machine, dialysis machines and cochlear implants, will be presented and assessed during the lecture. A very important part of computational intelligence application in biomedical engineering is telemedicine. Using modern sensors (including wearable ones) and applying current methods of wireless signal transmission we can collect a lot of medical data taken from many users. Practical use of these data (e.g. for selecting rare endangered persons, who need quick help, from big population of telemedically monitored people) need computational intelligence support. All mentioned above examples lead to conclusions, that both modern biomedical engineering need computational intelligence and developed computational intelligence tools can be implemented in biomedical engineering.


Evolutionary Algorithms as a Dynamical Complex Systems
Recent Advances and Progress

Ivan Zelinka
Department of Computer Science, Faculty of Electrical Engineering and Computer Science VŠB-TUO, Czech Republic

Ivan Zelinka (born in 1965, is currently associated with the Technical University of Ostrava (VSB-TU), Faculty of Electrical Engineering and Computer Science. He graduated consequently at the Technical University in Brno (1995 - MSc.), UTB in Zlin (2001 - Ph.D.) and again at Technical University in Brno (2004 - Assoc. Prof.) and VSB-TU (2010 - Professor). Prof. Zelinka is responsible supervisor of several grant researches of Czech grant agency GAČR as for example Unconventional Control of Complex Systems, Security of Mobile Devices and Communication (bilateral project between Czech and Vietnam) and co-supervisor of grant FRVŠ - Laboratory of parallel computing amongst the others. He was also working on numerous grants and two EU projects as member of team (FP5 - RESTORM) and supervisor (FP7 - PROMOEVO) of the Czech team. He is also head of research team NAVY Prof. Zelinka was awarded by Siemens Award for his Ph.D. thesis, as well as by journal Software news for his book about artificial intelligence. He is a member of the British Computer Society, Machine Intelligence Research Labs (MIR Labs -, IEEE (committee of Czech section of Computational Intelligence), a few international program committees of various conferences, and three international journals. He is also the founder and editor-in-chief of a new book series entitled Emergence, Complexity and Computation (Springer series 10624, see also

This tutorial is focused on recent progress (after 4 years of research and support of the fundamental research grant) in mutual intersection of a few interesting fields of research whose core topic are evolutionary algorithms in general. It discusses recent progress in evolutionary algorithms that can be considered discrete dynamical complex system with inherent nonlinear dynamics that is visualized and analyzed as a complex network and CML (coupled map lattices) systems. As already reported in many research papers and books, this dynamics can generate different kinds of behavior including chaotic one and can be visualized as a complex geometrical structure. In the tutorial there will be explained relations between evolutionary dynamics, their visualization as a complex network and actual state of our novel methods of their control.
Selected evolutionary algorithms in this tutorial will be differential evolution, genetic algorithm, particle swarm, Bee algorithm, and others. Methodology, converting evolutionary algorithms to the complex network will be introduced including demonstrations. Relation between complex networks parameters, including their time dependence, and evolutionary dynamics will be explained. Tutorial will then continue by explanation how evolutionary dynamics san be converted into so called CML systems, that are used to model spatiotemporal behavior (including chaotic one) and there will be explained its relation to complex networks (i.e. to evolutionary dynamics). At the end will be demonstrated how we can control EAs dynamics by means of feedback loop control scheme. Reported methodology and results are based on actual state of art (that is a part of this tutorial) as well as on our own research.
Tutorial is designed as an introduction; no advanced or expert knowledge from complex networks, chaos and control is expected. Its structure, results and content is based on our previous research, journal publications, keynotes and tutorials and reflect the latest progress on that field.
Tutorial structure:

  • Evolutionary algorithms – is evolutionary algorithm a dynamical system?
  • Evolution as a complex (social-like) network – how can be EA converted to complex network and what benefit we can get? Analysis, control…
  • Visualization of evolution as a CML system – EA and its complex network counterpart can be also converted to the CML system. What analysis and controllability of EA can be done on this? Does EAs generate chaos in its internal dynamics? What are relations with EA performance and special regimes inside EA dynamics (chaos, hidden attractors, etc…)
  • Conclusion - EAs can be viewed, analyzed and controlled like complex networks and CML systems.