Special Sessions

In the nature there are many examples that could help humanity to develop new projects, to improve and to solve some real life complex problems. The Bio-inspired Fuzzy Systems have the ability to include both natural computing and real life coefficients of uncertainty to keep in balance the solutions of the large-scale static and dynamic problems. The strategies of natural organisms (as ants, bees, nano-bots, swarms, flocks etc.) include adaptation and learning based on environmental changes, incomplete input information and the presence of noise. That is why Artificial Intelligence uses bio-inspired techniques, like ant colonies, artificial immune systems, swarm intelligence, neural networks, evolutionary computation, and not at last fuzzy logic to solve difficult problems.
The aim of this special session is provide an opportunity for international researchers to share and review recent advances in the foundations, integration architectures, and applications of Bio-inspired Fuzzy Logic systems in Pattern Recognition, Bioinformatics and computational biology, Healthcare, Industry, Microelectronics, Transportation, Green Logistics, Social Network, Web services, Cloud Computing and other domains.

Rabie A. Ramadan

Cairo University

Valentina E. Balas

University “Aurel Vlaicu” Arad , Romania

Camelia Pintea

Universitatea Tehnica Cluj-Napoca Cluj-Napoca, Romania Join institution

Mario Pavone

University of Catania

Nicolaie Popescu-Bodorin

University of S-E Europe LUMINA, Bucharest, RO

Ahmad Taher Azar

Benha University

Recent developments in sensor and actuator technologies have stimulated much research in unmanned aerial vehicles (UAVs). On one hand, we are aware of the fact that quadrotors have been becoming a part of our daily life day to day; on the other hand, their control is still a challenging task as, unlike from the ground vehicles, they do not have enough friction forces to stabilize their motion. So far, model based controllers, in particular model predictive control and linear quadratic regulators, are widely implemented in aerial vehicles as they can deal with multi input multi output systems. However, modeling stage of complex aerial vehicles requires tremendous man power and expertise because of their highly nonlinear dynamics as well as complex inter couplings. In this case, their model free control is more than welcome.
The special session will aim to gather papers in practical guidance, navigation and control of aerial robotics by using intelligent control techniques with a mix of fundementals and real time applications. The special session will also try to present the state of the art aerial robot design, state and parameter estimation, perception and planning by using intelligent techniques.

Erdal KAYACAN

Nanyang Technological University

Mojtaba Ahmadieh Khanesar

K. N. Toosi University of Technology

Mehmet Onder Efe

Hacettepe University

Over the past few decades, research and applications of different learning algorithms for the training of the structure of neuro-fuzzy systems, type-1 or type-2 cases, has attracted growing attention from both scientific and industrial communities. With the ever increasing need for computational power particularly in areas of identification, pattern recognition, big data computing, fault detection, etc., different learning methods are proposed which offer important advantages such as fast learning speed, ease of implementation, rigorous stability analysis and fewer design parameters.
This special session aims to serve researchers and developers to publish original, innovative and state-of-the art algorithms and architectures for different training algorithms for neuro-fuzzy structures, type-1 or type-2 cases, and their real time applications.

Erdal KAYACAN

Nanyang Technological University

Mojtaba Ahmadieh Khanesar

K. N. Toosi University of Technology

Mohammad Biglarbegian

University of Waterloo

The term Soft Computing, also widely known as Computational Intelligence, is usually used in reference to a family of several preexisting techniques (Fuzzy Logic, Neuro-computing, Probabilistic Reasoning, Evolutionary Computation, etc.) able to work in a cooperative way, taking profit from the main advantages of each individual technique, in order to solve lots of complex real-world problems for which other techniques are not well suited. In the last few years, many software tools have been developed for Soft Computing. Although a lot of them are commercially distributed, unfortunately only a few tools are available as open source software. Please, notice that such open tools have recently reached a high level of development. As a result, they are ready to play an important role for industry and academia research.
The aim of this session is to provide a forum to disseminate and discuss Software for Soft Computing, with special attention to Fuzzy Systems Software. We want to offer an opportunity for researchers and practitioners to identify new promising research directions in this area.

Jesús Alcalá-Fdez

University of Granada, Spain

José M. Alonso

University of Santiago de Compostela, Spain

Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, fuzzy sets and systems have been proposed as a useful and effective framework for the analysis of brain activity as well as to enable a direct communication pathway between the brain and external devices (brain computer/machine interfaces). While there has been increasing interest in these questions, the contribution of fuzzy logic sets and systems has been diverse depending on the area of application. On the one hand, considering the decoding of brain activity, fuzzy sets and systems represent an excellent tool to overcome the challenge of processing extremely noisy signals that are very likely to be affected by non-stationarities. On the other hand, as regards neuroscience research, fuzziness has equally been employed for the measurement of smooth integration between synapses, neurons, and brain regions or areas.
In this context, the proposed special session aims at providing an encounter and specialised forum for researchers interested in employing fuzzy sets, logic and systems for the analysis of brain signals and neuroimaging data, including related disciplines such as computational neuroscience, brain computer/machine interfaces, neuroscience, neuroinformatics, neuroergonomics, affective neuroscience, neurobiology, brain mapping, neuroengineering, and neurotechnology.

Chin-Teng Lin

University of Technology Sydney

Javier Andreu-Perez

Imperial College London

Although language, or linguistic expressions, undoubtedly contains fuzziness in nature, very little research has been conducted in related fields in recent years, as it was shown in “A Critical Survey on the use of Fuzzy Sets in Speech and Natural Language Processing”, Proc. of the IEEE WCCI 2012, Brisbane, Australia. This is partly because of the prevalence of probabilistic machine learning technologies in the natural language processing field. However, there has been a growing recognition that fuzziness found in every aspect of human language has to be adequately captured and that recent developments in the fields of computational intelligence such as computing with words can make a contribution. This session will follow on from the successful, special session entitled “Fuzzy Natural Language Processing” which was held at IEEE FUZZ 2015 in Istanbul and IEEE FUZZ 2013 in India and the hybrid special sessions held at the 2014 IEEE WCCI in Beijing and the 2016 IEEE WCCI in Vancouver.
The aim of this Special Session is therefore to explore new techniques and applications in the field of fuzzy natural language processing which capture the fuzzy nature of human language.
The session will provide a forum to disseminate and discuss recent and significant research efforts in fuzzy paradigms and applications in the field of fuzzy natural language processing.

Dr Keeley Crockett

Manchester Metropolitan University, UK

Professor Joao Paulo Carvalho

Instituto Superior Técnico, Technical University of Lisbon, Portugal

Type-2 fuzzy logic control is a technology which takes the fundamental concepts in control from type-1 fuzzy logic and expands upon them in order to deal with higher levels of uncertainty presented in many real-world control problems. A variety of control application areas have been addressed with type-2 fuzzy logic, from the control in steel production plants to the control of marine diesel engines and robotic control. For some engineering applications, there is evidence that type-2 fuzzy logic can provide benefits over both traditional forms of control as well as type-1 fuzzy logic.
It is the aim of this special session to attract a comprehensive selection of high quality current research in this area of type-2 control, motivating further collaboration and providing a platform for the discussion on future directions of type-2 fuzzy logic control by active researchers in the field. This special session will address advances in interval type-2 as well as general type-2 fuzzy logic control.

Tufan Kumbasar

Istanbul Technical University

Erdal Kayacan

Nanyang Technological University

Hao Ying

Wayne State University

Evolving systems are modular systems that simultaneously develop their structure, functionality, and parameters in a continuous, self-organized, one pass adaptive way from data streams.
During the last 12-15 years, the concept of Evolving Fuzzy Systems (EFS) established as a useful and necessary methodology to address the problems of imprecision, incremental learning, adaptation and evolution of fuzzy systems in dynamic environments and during on-line/real-time operation modes. EFS are able to automatically and autonomously adapt themselves to new operating conditions and system states and hence guarantee a high process safety, especially in case of highly dynamic and time-variant systems. This is especially necessary when precise and sufficient training data is not available (e.g., because of high costs for data collection or annotation) in order to set up models which cover the whole range of possible system states. In other cases, drifts or shifts in the systems may appear (due to environmental changes or changes in system modes and interrelations), which cannot be appropriately handled with standard fuzzy systems learning methods. EFS significantly contributes in this field of research by assuring flexible models and robustly outweighing of older learned behaviors smoothly over time. Another major topic which can be addressed with EFS are the building of models from huge massive stream data or even from Big Data, and to serve as dynamically adaptable knowledge base within enriched human-machine interaction applications (learning and teaching).
The goal of the special session is to provide a broad picture of the recent developments and to explore further (open) research challenges in one or several specific research topics mentioned belonging to one of these areas: (1) novel adaptive, incremental methods in evolving fuzzy modeling tasks; (2) enhanced issues in dynamic fuzzy methods; (3) real-World applications of evolving fuzzy systems.

Plamen Angelov

Lancaster University, UK

Fernando Gomide

University of Campinas, Brazil

Edwin Lughofer

Johannes Kepler University Linz, Austria

Igor Skrjanc

University of Ljubljana, Slovenia

The Session on “Cognitive Systems” provides an interdisciplinary forum for researchers and developers to present and discuss experiences, ideas, and research results in the Cognitive Computing area. This is an emerging research area, investigating the development of cognitive systems, able to learn, reason, and pervasively engage with humans in a natural, personalized, reactive and/or proactive way. This new generation of intelligent systems is mainly devised for specific application domains with the aim of providing decisional support for solving complex problems. Original contributions are sought, covering the whole range of theoretical and practical aspects, technologies and systems in such a research area. In particular, the Session will focus on methodologies, algorithms and techniques for the automatic learning and human-like reasoning, the identification of human activities and behaviors in specific contexts and the automatic adaptation in response to external dynamics, the natural human-computer interaction, the management and semantic integration of huge amounts of heterogeneous data. Submitted papers will be evaluated on the basis of significance, originality, technical quality, and exposition. Papers should clearly establish their research contribution and the relation to the goals of the Session.

Massimo Esposito

Institute for High Performance Computing and Networking (ICAR) of National Research Council of Italy (CNR)

Type-2 fuzzy sets and systems are paradigms which seek to realize computationally efficient fuzzy systems with the ability to give excellent performance in the face of highly uncertain conditions. Specifically, type-2 fuzzy sets provide a framework for the comprehensive capturing and modelling of uncertain data, which, together with approaches such as clustering and similarity measures (to name but two) provides strong capability for reasoning about and with uncertain information sources in a variety of contexts and applications. Type-2 fuzzy systems combine the potential of type-2 fuzzy sets with the strengths of rule-based inference in order to provide highly capable inference systems over uncertain data which remain white-box systems (i.e. interpretable).
The aim of this special session is to present and focus top quality research in the areas related to the underlying theory of type-2 fuzzy sets and systems. There are many open and unanswered questions about properties and nature of type-2 fuzzy sets and systems, this session is designed to provide a forum for the academic and industrial communities to report on advances in including, but are not limited to representations of type-2 fuzzy sets, approaches to defuzzification, fuzzy operators, fuzzy measures, interpretability, computational complexity, related extensions to type-1.

Robert John

University of Nottingham

Simon Coupland

Demontfort University

Josie McCulloch

University of Nottingham

Big Data has emerged as a hot topic in the recent years. It refers to those advantages, and also challenges, derived from collecting and processing vast amounts of data. The benefits from the management of these types of problems is clear: the larger the data, the higher the degree of knowledge that can be extracted from it. In addition to the former, the speed rate of incoming information is becoming higher and higher. Finally, the different sources that carry out the data recording also implies an heterogeneous structure.
We must emphasize the necessity of working in the scenario of Big Data. Clearly, having a larger amount of data can allow researchers and corporations to extract better and more useful knowledge from the applications they are working with. By achieving higher quality results from data, implies a stronger support for taking future decisions on this context.
The Big Data problem is a recent field of study, and therefore there are just few works which address this topic from the perspective of fuzzy modeling. Their ability to provide a better representation of the problem space by means of fuzzy labels and/or fuzzy sets, makes them a very interesting approach when both the volume and variety of the dataset increases. Additionally, experts can benefit from the interpretability associated to linguistic labels.
New research in the Big Data scenario for fuzzy modeling must be focused on re-designing the state of- the-art algorithms, as well as novel approaches for recent work scenarios.
This special session aims to provides innovative approaches to handle various fuzzy issues in big data presentation, processing and analysing by applying fuzzy sets, fuzzy logic and fuzzy systems.

Francisco Herrera

University of Granada, Spain

Alberto Fernández

University of Granada, Spain

Energy use is an increasing concern in data science. Recent research promote algorithms optimizing the number of computations per kilowatt-hour over the number of computations per second. In this respect, the concept of eco-efficiency brings together economic and environmental factors for a more efficient use of resources and lower emissions. Eco-efficiency is represented by the quotient between the service value and its environmental influence, that affects both energy consumption and greenhouse gas emissions.
In the context of data science, the environmental efficiency is accounted for through quality metrics that combine energy consumption and sustainability. Soft computing techniques are key in the design of these emerging quality metrics because, in many cases, partial preference orderings or analogous descriptions of uncertainty are involved when the different models are compared. Learning algorithms are being developed that are able to exploit these preferences, but many theoretical, methodological and practical issues about eco-efficiency in data science are still unsolved.

Luciano Sánchez

Universidad de Oviedo

José Ranilla

Universidad de Oviedo, Spain

Sebastien Destercke

Université de Technologie de Compiegne, France

Type-2 fuzzy sets and systems are paradigms which seek to realize computationally efficient fuzzy systems with the ability to give excellent performance in the face of highly uncertain conditions. Specifically, type-2 fuzzy sets provide a framework for the comprehensive capturing and modelling of uncertain data, which, together with approaches such as clustering and similarity measures (to name but two) provides strong capability for reasoning about and with uncertain information sources in a variety of contexts and applications. Type-2 fuzzy systems combine the potential of type-2 fuzzy sets with the strengths of rule-based inference in order to provide highly capable inference systems over uncertain data which remain white-box systems (i.e. interpretable).
The aim of this special session is to present and focus top quality research in the areas related to the practical aspects and applications of type-2 fuzzy sets and systems. The session will also provide a forum for the academic community and industry to report on recent advances within the type-2 fuzzy sets and systems research.

Christian Wagner

Michigan Technological University, USA and University of Nottingham, UK

Hani Hagras

University of Essex, UK

Jonathan M. Garibaldi

University of Nottingham, UK

Fuzzy inference is widely used in many aspects of problem solving, including data-mining, prediction, image and natural language processing. Here, it has been applied to a multitude of applications ranging from robotics to medicine and biology. While both type-1 and type-2 fuzzy logic systems have and are being developed, the vast majority of these systems are based on singleton Mamdani or TSK inference.
This special session focuses specifically on novel innovations which drive non-standard approaches to reasoning and fuzzy inference. Non-standard here refers to fuzzy inference systems which differ at a key stage from standard approaches to fuzzy inference. This includes differences in the fuzzification, rule combination, or defuzzification stages, as well as novel operators such as t-norms and t-conorms and their application.
The most prominent non-standard approach, which has recently seen a surge of innovative research, is non-singleton inference, which enables the direct capture of input uncertainties and their incorporation into the reasoning process. While these systems have been shown to deliver excellent performance, often superior to that of singleton systems, they also offer unique potential in developing design approaches for fuzzy inference systems where uncertainty at each stage of the system is modelled individually. Beyond non-singleton approaches at the system output stage, novel approaches which address the rule combination stage provide exciting ways of addressing both theoretical and real world challenges.
The aim of this special session is to bring such innovations around fuzzy inference and reasoning together and to provide a common forum for discussing the future development (and development needs) of fuzzy inference systems. Both theoretical advances in methods of inference and their applications will be of key interest.

Timothy C. Havens

Michigan Technological University, USA

Christian Wagner

Michigan Technological University, USA and University of Nottingham, UK

Derek T. Anderson

Mississippi State University, USA

Data mining is a very active research field because there is a vast number of real-world problems that can be tackled using techniques encompassed by this area of research. In this field there can be found classical problems like classification, regression or clustering. Besides them, during the last years a great number of challenging problems have emerged like the problem of imbalanced data, multi-label and multi-instance problems, low quality and/or noisy instances or semi-supervised learning among others. Furthermore, data preprocessing techniques play a vital role for the subsequent success of machine learning techniques.
Learning methods based on soft computing techniques are widely used to face the aforementioned problems. Fuzzy systems have demonstrated the ability to provide an interpretable model understandable by human beings whereas the results obtained are accurate due to their ability to cope with the great uncertainty inherent to these new challenging problems. Evolutionary computation is a robust technique for optimization, learning and adaptation tasks. They can adapt the model parameters for each problem to obtain a highly accurate system. Their synergy implies a better capability for the design and optimization of fuzzy models. Moreover, in the era of Big Data new possibilities for fuzzy methods emerge in the context of data mining. There are new challenges regarding the scalability of fuzzy algorithms when addressing very large datasets.
The aim of the session is to provide a forum to disseminate and discuss recent and significant research efforts on soft computing techniques based on fuzzy logic to deal with data mining problems, in order to deal with the current challenges on this topic. The special session is therefore open to high quality submissions from researchers working in this research field.

Mikel Galar

Public University of Navarre

José Antonio Sanz

Public University of Navarre

There is a need to increase autonomy in modern security, defence, and sensing applications in order to reduce the burden on the operator and increase the spectral and geospatial coverage of modern high bandwidth and networked sensor systems. This need spans many applications, examples may include CCTV security systems, in-home monitoring sensors (e.g. falls detection), healthcare sensors (e.g. ECG monitors), indoor positioning and locating sensors (e.g. iBeacon systems) and many, many others. A common requirement across all of these sensing applications is how to ensure that the outputs of the sensors accurately and robustly replicate a decision that would otherwise be made by a human operator.
As an example, we have seen a considerable effort in recent years into increasing the autonomy of sensor-based surveillance systems, resulting in platforms that can recognise traffic infringements, unauthorised people entering areas, loitering, tailgating, etc. Performance of these is improving dramatically and, with the increased penetration of small, powerful, and embedded computing devices, holds the potential to further enhance the autonomy of sensor systems and provide accurate and robust decision support to human operators.
The aim of this special session is to present the latest research into the theory, application, and development of new computational methods that will lead to further increases in system autonomy. We welcome high quality, original research from both the academic community and industry, to report on recent advances in this area.

Dr Jason J. Lepley

Leonardo

This special session aims to present highly technical papers describing new FCM models and methodologies addressing any of the following specific topics: theoretical aspects, learning algorithms, innovative applications and FCMs extensions. During the past decade, FCMs played a vital role in the applications of diverse scientific areas, such as social and political sciences, engineering, information technology, robotics, expert systems, medicine, education, prediction, environment etc.
Fuzzy Cognitive Map is an extension of cognitive maps for modeling complex causal relationships easily, both qualitatively and quantitatively. As a Soft Computing technique it is used for causal knowledge acquisition and providing causal knowledge reasoning process. FCMs have emerged as tools for representing and studying the behavior of systems and people. By combining the main aspects of fuzzy logic, neural networks, expert systems, semantic networks, they have gained considerable research interest and are widely used to analyze causal complex systems. FCMs can be constructed from raw data as well. FCMs model any real world system as a collection of concepts and causal relation among concepts. From an Artificial Intelligence perspective, FCMs are dynamic networks with learning capabilities, whereas more and more data is available to model the problem, the system becomes better at adapting itself and reaching a solution. They gained momentum due to their dynamic characteristics and learning capabilities. These capabilities make them essential for modeling and decision making tasks as they improve the performance of these tasks.
This Special Session is dedicated to providing presentaions with deep insights on fundamentals, modeling methodologies, learning algorithms, optimization, convergence issues for fuzzy cognitive maps (FCMs), supplemented with applications on real case studies using FCMs and their extensions, enhanced structures and dynamic capabilities.

Papageorgiou Elpiniki

University of Applied Science, Greece

Engin Yesil

Getron Corp. Hoboken Riverfront Center, USA

Aggregation theory is a field in rapid progress. Apart from their obvious interest from a theoretical point of view, they are an essential tool for applications in many different fields, such as image processing, classification, machine learning or decision making among many other. For this reason, in recent years there is a growing interest in the topic, which is leading to the introduction of novel concepts and approaches.
This session will focus on recent theoretical developments in the fields of aggregation functions, and aims at bringing together leading researchers in the field in order to present their most recent developments and for discussing recent trends in this area, as well as to identify potential problems of interest for researchers. In particular, but not limited to, this special session will deal with research in fields such as t-norms and t-conorms, overlap and grouping functions, means, uninorms, and in general, any type of aggregation function both in the real setting and in lattices.

Humberto Bustince

Public University of Navarra

Radko Mesiar

Slovak University of Technology

Javier Montero

Complutense University of Madrid

Javier Fernandez

Public University of Navarra

This session will deal with applications where aggregation functions play a crucial role. These applications may include, but are not limited to, image processing, data mining, big data, classification and decision making. The considered applications may make use either of real aggregation functions or of more general aggregation functions, as those defined over lattices or extensions of fuzzy sets. We hope to provide a forum to discuss up-to-date applications in which the usefulness of recent developments in aggregation functions is clear.
This special session is co-organized by the EUSFLAT working group on Soft Computing methods in Image processing. We want to put a bridge between aggregation and convolution/deconvolution in sampling/upsampling, denoising, fusion and other relevant problems.

Humberto Bustince

Public University of Navarra

Radko Mesiar

Slovak Univeristy of Technology

Javier Montero

Complutense University of Madrid

Irina Perfilieva

University of Ostrava

Daniel Paternain

Public Univeristy of Navarra

This special session will consider generalizations of the notion of aggregation functions which have appeared in the literature in recent years and which lead to new classes of functions that encompass both classical aggregation functions and other functions which are relevant, specially from the point of view of the applications, but which do not fulfil all the conditions required to an aggregation. This session will be open to both theoretical work and applied works which may justify the use of the considered generalizations.

Humberto Bustince

Public University of Navarra

Radko Mesiar

Slovak University of Technology

Javier Montero

Complutense University of Madrid

Aranzazu Jurio

Public University of Navarra

Real world decision problems are often defined under uncertain environments, with imprecise information, is straightforward the use of linguistic information due to the nature of different aspects of the decision problems. Fuzzy linguistic approach and Computing with Words (CW) provides the tools and methodology to deal with words or sentences defined in a natural or artificial language instead of numbers. However the complexity of decision problems make this topic to enhance previous models and methods constantly, rising new proposals such as the recent hesitant linguistic fuzzy sets, Linguistic Distribution Assessments, etc., for modelling linguistic information and their application to novel and classical problems with the view to improve the resolution of decision making under uncertainty.
All these models and their application to different complex decision real world problems have recently attracted much attention in which, novel mathematical foundations and new decision models risen to be applied in different decision fields such as multi-criteria decision making, decision analysis, evaluation processes, consensus reaching processes, emergency decision making, information fusion, etc.

Luis Martínez

University of Jaén

Francisco Herrera

University of Granada

The aim of this special session is to present the recent advances in the theory and practice of the fuzzy-logic systems in solving complex aerospace problems. The main focus of this session is on modeling, identification, and control of aerospace vehicles including unmanned aerial vehicles (UAVs), aircraft, vertical take-off and landing (V/TOL), and spacecraft.

Mohammad Ayoubi

Santa Clara University

Kelly Cohen

University of Cincinnati

For more than two decades, evolutionary computation and various meta-heuristics have frequently been used for fuzzy system design under the name of evolutionary fuzzy systems. Their learning and adaptation capabilities enable structure and parameter optimization of fuzzy systems for many kinds of machine learning tasks such as modeling, classification, and rule mining. Their flexible frameworks also enable to handle multiple objectives like accuracy and interpretability maximization and many kinds of data types like imbalanced, missing, and privacy-preserving data sets. Evolutionary fuzzy systems have frequently been used in a wide variety of applications. For example, the recent success of genetic fuzzy tree (ALPHA) proves the great potential of evolutionary fuzzy systems in AI.
The aim of this session is to provide a forum to disseminate and discuss recent and significant research efforts on evolutionary fuzzy systems in order to deal with current challenges on this topic.

Yusuke Nojima

Osaka Prefecture University

Rafael Alcalá

University of Granada

Hisao Ishibuchi

Osaka Prefecture University

This session aims to demonstrate how the foundations of many-valuedness, as enabling non-commutativity for its related operators as typically appearing in quantales and modules, provide a thematic and applicative aspect overlaying the formal treatment of many-valued order. This session and its scope opens up new avenues for approaches to many-valued mathematics, and the way related methodology becomes applicative in various areas of research. The foundations of many-valuedness, as enabling non-commutativity for its related operators, is another thematic and applicative aspect overlaying the formal algebraic treatment. The session indeed aims to exploit the benefit of using semigroups in complete lattices with focus on quantales and modules, and as it enables new approaches to many-valued mathematics, in particular as related to algebraic, logical and topological methodology.
Since the mid 1980's, quantales find an increasing interest in various areas of mathematics. This development has its origin in ideas coming from non-commutative geometry and is driven by the desire to create a non-commutative and nonidempotent set theory known as quantale sets. Since that time, an extensive literature has grown and is scattered over a diversity of journals. Furthermore, and as algebra at the same time became connect with developments using category theory, dedicated workshops and conferences emerge to focus on enriched category theory in further support of studying quantales.
The session also encourages application development even in situations where non-commutativity and many-valued order is used as a applicative mechanism in information structures as appearing in real applications. This ambition strives to demonstrate how complicated theory is required for solutions in complicated applications. The scope of this sessions has its focus on non-commutativity for the foundations of many-valuedness, on the one hand, as it typically appears in mathematical disciplines like algebra, logic, topology, and other related and similar disciplines, and, on the other hand, as it appears in computer and information sciences as e.g. related to structures of programming languages and analytics of typed information. The slogan is "many-valued and non-commutative structures are everywhere", i.e. going far beyond just saying "fuzzy sets are everywhere". As the ambition is that theory meets practice, the session invites to interdisciplinary thinking, yet building upon a comprehensive and coherent representation of non-commutativity and many-valued order in theory and practice.

Patrik Eklund

Umeå University

Ulrich Höhle

Bergische Universität Wuppertal

Jari Kortelainen

Mikkeli University of Applied Sciences

Fuzzy set theory is the subject of deep investigation in fields such as engineering systems and related applications. Typically, these fields are interdisciplinary with a wide range of theory and methodologies that are being investigated and developed.
Ontologies have been started to be considered to represent the knowledge in many domains. Their fuzzy counterpart, i.e., fuzzy ontologies, are defined as an extension of crisp ontologies by adding a set of membership degrees to each concept of the domain ontology and adding fuzzy relations among these fuzzy concepts.
The main scope of this call is about fuzzy knowledge-based/ontology-based engineering systems and their application.

Hajer Baazaoui Zghal

University of Manouba, Tunis, Tunisia

Umberto Straccia

Istituto di Scienza e di Tecnologie dell’Informazione (ISTI) of the Italian National Council of Research (CNR)

Mourad Abed

University of Valenciennes - LAMIH (Human- Computer Interaction and Automated Reasoning research group)

The aim of this special session is to discuss and disseminate the most recent advancements focused on interpretability of fuzzy systems. Many fuzzy algorithms and models are indeed aimed at extracting knowledge from data, and the acquired knowledge must be usually communicated to users. However, as far as such knowledge is difficult to understand by users, the acceptance of such methods may be seriously compromised. Interpretability must be the central point on system modeling. In fact, some of the hottest and most recent research topics like Precisiated Natural Language, Computing With Words, and Human Centric Computing strongly rely on the interpretability of the designed models. The challenge is to better exploit fuzzy logic techniques for improving the human-centric character of many intelligent systems.
Interpretability is therefore a key property of intelligent systems of practical use, but its human-centric character poses difficult challenges both in design and evaluation. The issue of interpretability is tackled in different ways by focusing on specific facets of system modeling, such as the formalization of interpretability in an algebraic structure, or the definition of constraints to be imposed during model design and tuning. Moreover, the evaluation of interpretability is a mandatory issue to validate and compare models.

Ciro Castiello

University of Bari ''Aldo Moro Bari'', Italy

Corrado Mencar

University of Bari ''Aldo Moro'', Italy

José M. Alonso

University of Santiago de Compostela, Spain

Geosciences are fields related to the usage of computer sciences to solve problems of Earth, atmospheric, oceanographic, and environmental sciences. The interdisciplinary technologies have been applied to many real-life problems in geosciences, including soft computing techniques. Today, computer and geosciences play an important role in bringing the advanced technologies to the life. It has been already demonstrated that fuzzy logic offers a wealth of effective techniques for solving many problems in geosciences.
Authors are invited to submit their original and unpublished work in the following areas including (but not limited to): fuzzy systems and hybrid systems for Geosciences, fuzzy clustering in remote sensing image processing, aAdvanced fuzzy sets (type-2/intuitionistic fuzzy sets, etc.) and systems for applications in geosciences, multi-spectral/Hyper-spectral satellite image analysis and Geography Information Systems.

Long Thanh Ngo

Le Quy Don Technical University, Vietnam

Witold Pedrycz

University of Alberta, Canada

Cluster analysis is a well-known set of techniques and algorithms which seeks to form groups of data with both strong internal similarity and strong external dissimilarity. In practice, this task is made harder by the presence of information in the data that is irrelevant to form the clusters, such as additional features, especially in the context of "big data" and high-dimensional data in general.
To face this difficulty, in the recent years there has been a growing interest for new clustering techniques which identify clusters by means of different subspaces from the original data space, for example by weighting these features, identifying linear subspaces where the data reside, or forming groups of correlated features which lead to clusters with high density. As for traditional clustering approaches, there exists a great diversity of choices and options regarding subspaces of interest. Various approaches to tackle this problem have been proposed by different communities of researchers. Some of the best known of these groups include "subspace clustering", "projected clustering", "coclustering" (or "biclustering"), and "correlation clustering".
This special session proposal aims to offer the opportunity for these communities to meet and exchange, with the specificities of soft clustering in mind.

Arthur Guillon

University Pierre and Marie Curie

Marie-Jeanne Lesot

University Pierre and Marie Curie

Christophe Marsala

University Pierre and Marie Curie

Nikhil R. Pal

Indian Statistical Institute

In modern biomedical research, large sets of digital data are retrieved from a plethora of molecular, cellular and systemic experiments, which are possible thanks to the rapidly evolving high-throughput technologies, such as the next generation sequencing. Biomedical imaging informatics has become a crucial part of modern healthcare, clinical research and basic biomedical sciences. Rapid improvement of imaging technology and advancement of imaging modalities in recent years have resulted in a significant increase in the quantity and quality of such images. The identification of new strategies for analyzing such data is becoming more and more necessary since the large amount of data can sometimes represent a real obstacle to effectively identify the most relevant patterns and to build comprehensive models capable of explaining complex biological phenotypes.
Particularly, Soft Computing and Data Mining techniques are already emerged as powerful tools to mine and interpret this kind of data and promise to pave the way for ever more techniques to take on this challenge.
The aim of the special session is to host the recent research advances in the fields of Soft Computing methodologies concerning biomedical data.
Potential topics include Soft Computing methodologies (but are not limited to): Fuzzy Logic (i.e., algebras, norms, connectives, relations, reasoning, inferences, …); Rough-Sets; Neuro-Fuzzy; Granular Computing; Data Mining; when applied to: OMICs and high-throughput data in the broad context of genomics, epigenomics, transcriptomics and proteomics; models capable of explaining the pathogenesis or predicting the predisposition and/or the clinical outcome of human diseases; the evaluation of protein folding and/or protein-ligand interactions (where ligands are proteins, DNA, RNA and small molecules), also in the context of genetic variation; the identification of potential gene regulatory elements (i.e., binding of transcription factors, miRNAs, etc.); analysis of common genetic variants (i.e., SNPs, HLA genotypes, microsatellites); analysis of experimental data from next generation sequencing; analysis of gene expression data; analysis of medical imaging data; biomedical applications. Furthermore, software tools designed for addressing any of the above topics, might be also considered relevant for the special session.

Angelo Ciaramella

Università degli studi di Napoli “Parthenope”, Italy

Sushmita Mitra

Machine Intelligence Unit, Indian Statistical Institute, India

Antonino Staiano

Università degli studi di Napoli “Parthenope”, Italy

The development of human–computer interaction systems based on natural language, already important in the last decades, is growing in importance nowadays. Particularly, data-to-text systems are intended to obtain a text describing the most relevant aspects of data for a certain user in a specific context. Such texts, called linguistic summaries and descriptions of data, are comprised of a collection of natural language sentences, and must be as close as possible to those generated by human experts. In this realm, not only specialized users (e.g. in decision support systems) are interested in this type of approach, but nonspecialized users also show interest in receiving understandable information that is supported by data.
Linguistic summaries commonly use fuzzy set theory to model linguistic variables and incorporate different forms of imprecision in a collection of natural language sentences. In many approaches they can be considered as quantifier based sentences, hence linguistic summaries constitute a perfect application for new developments in the domain of fuzzy quantifiers. Furthermore, linguistic summaries have been related to fuzzy rule systems.
Linguistic summaries and description of data is related to other research areas such as knowledge discovery in databases and intelligent data analysis, flexible query answering systems for data, human-machine interaction, uncertainty management, heuristics and metaheuristics, and natural language generation and processing. More recently, this field has been related to the linguistic description of complex phenomena and computing with words paradigms.
The objective of this special session is to provide a forum for researchers, from the above indicated areas, to present recent developments in linguistic summarizes and description of data as well as discuss how these different approaches can complement each other for the task of building such systems.
The session continues the series of special sessions on the topic organized by some of the organizers of this session in past conferences (IFSA 2015, FUZZ-IEEE 2015, FUZZ-IEEE 2016).

Daniel Sanchez

University of Granada, Spain

Nicolas Marin

University of Granada, Spain

Anna Wilbik

Eindhoven University of Technology

Rui-Jorge Almeida

Eindhoven University of Technology

The problem of finding appropriate rankings to evaluate and/or sort different alternatives appears in a variety of disciplines such as Economics, Political Sciences, or Artificial Intelligence, among many others. In most decision making problems we aim at selecting the optimal alternative from a certain collection of them. Usually, the available information includes some evaluation of the different alternatives under different states of nature. Sometimes, this evaluation is based on human perceptions and/or expert knowledge. Different streams of the literature make different assumptions about the nature of this information: numerical utilities, qualitative evaluations, fuzzy-valued utilities, partial preferences, etc. We usually need some aggregation procedure in order to merge our information under the different states of nature in order to produce a single ordering (partial or total) over the whole set of alternatives. Such an aggregation method allows to select the optimal alternative, or the collection of non-dominated ones. The study of different aggregation methods under the above different assumptions is an ongoing research field. Researchers interested either in theoretical aspects or in applications related to this topic are encouraged to contribute to this session.

Gisella Facchinetti

University of Salento, Italy

Universidad de Oviedo, Spain

Bice Cavallo

University of Naples "Federico II", Italy

Computer-Supported Collaborative Work has been defined as a generic term, which combines the understanding of the way people work in groups with diverse enabling technologies, such as computer networking, and associated hardware, software, services and techniques [1].
The recent increase on the use of mobile and wearable devices, and emerging technologies such as virtual reality, augmented reality, the internet-of-things, among others, have opened up possibilities for enhancing collaborative work experiences; fostering participation, collaboration, creativity and engagement; and creating huge opportunities for integration and research. This has stimulated the need of computational intelligence applications in CSCW. Particularly, Fuzzy Logic (FL) has been used to handle uncertainty, with the aim of mimic human reasoning; and when combined with other soft computing techniques and computational intelligence methods, it has been used as a base to model, simulate, and create hybrid dynamic systems for collaborative telework, learning and training environments for skills transfer, and knowledge management.
Moreover, when coupling machine learning with context-aware knowledge representation, possibly using Fuzzy Logic or Case-based Reasoning, new models can be designed; supporting approaches such as Contextual Learning; where Information is presented in a way that the learners are able to construct meaning on their own experience, sharing this meaning with others, as well as letting the machine the ability to auto-adapt according to the indicators of this meaning. The application areas are numerous; and when applied to real-time decision making support, they construct knowledge from unstructured uncertain information data, with the challenge of measuring knowledge due to an ever-changing environment.
Therefore, the aim of this Special Session is to explore recent advances and applications of fuzzy logic, machine learning, and computational intelligence in the field of computer-supported collaborative work, with the aim to provide a forum to disseminate and discuss recent and significant research efforts in this field.
[1] Wilson, P. (1991). Computer Supported Cooperative Work: An Introduction. Kluwer Academic Pub.

Anasol Pena-Rios

University of Essex

Anne Liret

British Telecommunications / BT France

George Panoutsos

University of Sheffield, UK

The aim of this special session is to explore the uses of fuzzy systems in real world service operation applications. Fuzzy systems have demonstrated their capabilities in handling uncertainties. This makes them quite suitable for improving the performance and capabilities of real world applications. This uncertainty is particularly present in operational planning, where expected customer demand and expected available supply may not be easily predictable. The uncertainty is then also present from the types of customers to contact, to the delivery of the service, where that delivery relies on effective planning of resources.
The session will cover the latest developments in applications in service operations that utilise fuzzy logic systems. Applications should demonstrate a real world implementation. Potential benefits of the implementation of these systems should also be discussed.

Gilbert Owusu

BT Plc

Sid Shakya

BT Plc / EBTIC

Andy Starkey

BT Plc

Nowadays, robotics represents fields with a rapidly growing impact on a broad range of industrial and end users market sectors, including healthcare, agriculture, civil, commercial or consumer sectors, logistics, and transport. Nevertheless, its potential could be fully exploited only when robots have additional abilities such as reconfigurability, adaptability, interaction capability, dependability, decisional autonomy and cognitive capability. In this context, distributed reasoning capabilities provided by the integration of fuzzy logic and multi-agents methodologies may play a crucial role.
The aim of the Special Session is to provide a forum for experts in the development of multi-agent fuzzy systems to discuss the possible synergies between these research areas and open new perspectives for the development of cognitive robotics systems and applications.

Silvia Rossi

Università degli Studi di Napoli Federico II

Alessandro Di Nuovo

Sheffield Hallam University

Mariacarla Staffa

Università degli Studi di Napoli Federico II

The aim of this special session is to present the state-of-the-art results in the area of adaptive intelligent control theory and applications and to get together researchers in this area. Adaptive control is a technique of applying some methods to obtain a model of the process and using this model to design a controller. Especially, fuzzy adaptive control has been an important area of active research. Significant developments have been seen, including theoretical success and practical design. One of the reasons for the rapid growth of fuzzy adaptive control is its ability to control plants with uncertainties during its operation.
The papers in this special session present the most advanced techniques and algorithms of adaptive control. These include various robust techniques, performance enhancement techniques, techniques with less a-priori knowledge and nonlinear intelligent adaptive control techniques. This special session aims to provide an opportunity for international researchers to share and review recent advances in the foundations, integration architectures and applications of hybrid and adaptive systems.

Valentina E. Balas

University “Aurel Vlaicu” Arad , Romania

Tsung-Chih Lin

Feng-Chia University, Taiwan

Rajeeb Dey

CINVESTAV-IPN, Mexico

Yu-Chen Lin

Feng Chia University, Taiwan

Seshadhri Srinivasan

University of Sannio, Italy

The aim of this special session is to provide a forum: (1) To disseminate and discuss contemporary and significant research efforts in the field of fuzzy logic, security and forensics; (2) To promote both theoretical and practical applications of fuzzy logic in security and forensics; (3) To foster the integration of the academic community and industry who have been working in the field of fuzzy logic, security and forensics.

Nitin Naik

Defence School of Communications and Information Systems, Ministry of Defence, United Kingdom

Paul Jenkins

Defence School of Communications and Information Systems, Ministry of Defence, United Kingdom

The matrix factorization approach to fuzzy clustering is well documented in the literature. In 1974, Woodbury and Clive [WOO74] devised a method to estimate fuzzy partitions underlying multivariate categorical data, known as the grade of membership (GoM) model. They specifically focused on clinical data, and aimed to use the method for diagnostic and prognostic purposes. In 1990, Mirkin and Satarov [MIR90] proposed independently an extension of GoM analysis for modelling real-valued data as convex linear combinations of prototypes. Almost simultaneously, Cutler and Breiman [CUT94] presented their archetypal analysis, which received some popularity and following in the literature. The recent work by Ding, Li, and Jordan [DIN10], focusing on k-means clustering, gives another example of the application of matrix factorization approach to cluster analysis; see also [THU11] and particularly [SUL15] as an alternative way to perform fuzzy c-means clustering [BEZ81]. Therefore, we see the FUZZ-IEEE'2017 Conference as a good opportunity to integrate the matrix factorization within the mainstream of fuzzy data analysis. The matrix factorization approach to fuzzy clustering can be looked at from several perspectives. The potential contributors may wish to explore such research areas as: new approaches to prototypes, pattern recognition, computer vision, big data, cluster validation indices, estimation algorithms, software tools, and applications to any field of investigation where the fuzzy sets can be relevant.

[BEZ81] J.C. Bezdek Pattern Recognition with Fuzzy Objective Function Algorithms Plenum Press, 1981.
[CUT94] A. Cluter, L. Breiman Archetypal analysis Technometrics 36(4), 338-347, 1994.
[DIN10] C. Ding, T. Li, and M. Jordan Convex and semi-nonnegative matrix factorizations IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1, January, 2010.
[MIR90] B. Mirkin, G. Satarov Method of fuzzy additive types for analysis of multidimensional data I Automation and Remote Control 51(5), 683-688, 1990.
[SUL15] A. Suleman A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering Fuzzy Sets and Systems, 270, 90-110, 2015.
[THU11] C. Thurau, K. Kersting, M. Wahabzada, and C. Bauckhage Convex non-negative matrix factorization for massive datasets Knowledge and Information Systems 29 (2), 457-478, 2011.
[WOO74] M.A. Woodbury, J. Clive Clinical pure types as a fuzzy partition Journal of Cybernetics 11, 277-298, 1974.

Boris Mirkin

Higher School of Economics, Moscow RF and University of London, UK

Abdul Suleman

Instituto Universitário de Lisboa (ISCTE-IUL), Portugal

Historically, Fuzzy Logic has been successfully used to enable designers to model industrial controllers and decision making systems through simple linguistic rules. However, the design of a fuzzy system may be difficult if this must be implemented on different and heterogeneous hardware devices. As a consequence, it could be necessary to introduce standard and abstract languages for implementing fuzzy systems without considering technical constraints related to the real hardware platforms on which the fuzzy system will be run. In this context, on May 2016, IEEE Standards Association (IEEE-SA) published IEEE Std 1855™-2016, the first IEEE standard technology in the area of the Fuzzy Logic. IEEE Std 1855™-2016 is the IEEE Standard for Fuzzy Markup Language (FML), a markup language aimed at providing a unified and well-defined representation of Fuzzy Logic Systems (FLSs). This standard includes an extendable schema that natively defines the basic components of an FLS and enables the modeling of different categories of fuzzy inference engines, including Mamdani, Tsukamoto, Takagi-Sugeno- Kang (TSK), and AnYa. This standard allows fuzzy designers to simply code their ideas on heterogeneous hardware without need for a deep understanding of details related to the different platforms. In short, it makes it possible to model an FLS in a human-readable and hardware-independent way.
The aim of this session is to provide a forum to disseminate and discuss relevant researches in the area related to the modelling of FLSs for different domain applications by means of the IEEE Std 1855™-2016. Besides, this session aims to encourage the development of software to support the IEEE Std 1855™-2016 based modelling.

Giovanni Acampora

University of Naples Federico II, Italy

Nuptek Systems Ltd., Canada

Autilia Vitiello

University of Salerno, Italy

The theory and applications of fuzzy logic have been developed and described in many research papers and books. Fuzzy logic has been applied in engineering, the natural sciences, computer science, medicine, the social sciences, statistics, uncertainty management and risk analysis. However, the place of fuzziness in mathematics is still in question. Some mathematicians readily use and work with fuzziness; others do not use fuzziness; and some believe that fuzziness has no place in mathematics.
The purpose of this special session is to begin openly to discuss these differing points of view. Given that this is a fuzzy conference, the special session organizers expect that many of the contributions to this special session will support fuzziness in mathematics. Thus, perhaps the working title for this special session should be What Fuzzy Sets Bring to Mathematics.
The purpose of this special session is to begin openly to discuss these differing points of view. Given that this is a fuzzy conference, the special session organizers expect that many of the contributions to this special session will support fuzziness in mathematics. Thus, perhaps the working title for this special session should be What Fuzzy Sets Bring to Mathematics.
In particular, with this special session, we hope to expand the fuzziness-in-mathematics or the many-valuedness-in-mathematics discussion to include examples of mathematical situations which naturally encompass fuzziness or many-valuednesss. A reason for using “many-valuedness” is the foundations of fuzziness are not mathematical. However, the usual implementations of fuzziness in mathematics involve many-valuedness, and many-valuedness can be expressed in precise mathematics.
Authors submitting papers with examples of fuzzy sets or many-valuedness should clearly describe their examples, explain the importance of their examples, and show why/how the fuzzy sets are an integral and important part of the examples.

Austin Melton

Kent State University, USA

Didier Dubois

Stephen E. Rodabaugh

Due to the increasing of the system complexity in different technology areas, such as transport systems, renewable energy systems, power network, electric smart grids… it is of significance to address several fundamental problems regarding high-level systems reliability, safety and health monitoring of such systems.
This special session aims to present state-of-the-art development in the areas of health monitoring, fault estimation, detection and diagnosis, prognosis and tolerant control for nonlinear systems. Both theoretical and technology-based aspects for practical engineering systems will be considered, and recent theoretic study and application progress will be reported by academics, scientists and engineers. Indeed, there are still places for developing and improving the existing results in order to propose new techniques for fuzzy systems control and their healthy.

Hamid Reza Karimi

Politecnico di Milano

Mohammed Chadli

Université de Picardie Jules Verne

Peng Shi

The University of Adelaide

It is clear that computers have revolutionized information processing throughout all aspects of society. This is particularly true in the military domain, where sensors and vast amounts of data allow an unprecedented potential for real-time situational awareness by all echelons of command. There are many tasks, however, that humans still perform much better than computers. Examples of such tasks include relationship extraction, sentiment analysis, and other natural language processing tasks; planning, reasoning, and other complex cognitive tasks; recognition, classification, and other perceptual tasks; and the like.
Research in the field of “human computation”, in which humans participate along with machines as computational elements, is rapidly expanding. The focus of this special session is to explore the challenges of, and propose solutions for, organizing and including humans in fuzzy-based intelligent systems to leverage the complimentary abilities of humans and machines in the military domain. We are particularly interested in applying human computation to allow for increased situational awareness by commanders on the battlefield. This focus includes, but is not limited to, areas such as information foraging by automated agents, information aggregation, reasoning with complimentary and contradictory information, decision support applications, and other related areas.

Robert J. Hammell II

Towson University

Timothy P. Hanratty

U.S. Army Research Laboratory

Business processes define how the work is performed within an organization and improve the collaboration among all the involved stakeholders in enterprises and administrative entities of today’s world. The area of business process management (design, modeling, representation, monitoring, execution and discovery) is quite wide and several approaches in the last years were proposed to provide effective solutions to the problems that it raises.
In particular, when processes are not (or can not) purposefully designed and optimized and quickly change over time or are loosely-structured, most business process management algorithms still give unsatisfactory results. In those cases Fuzzy logic and approximate rule-based reasoning can help explicitly representing uncertainty and incomplete information in processes. One interesting area of Business Process Management that can benefit from the application of fuzzy theories is Process mining. It consists of a set of techniques and methods that collectively aims to extract compact and understandable representations of business processes from enterprise systems logs.
To this aim, the existing Process Mining approaches (e.g.traditional process mining algorithms like the alpha algorithm or those based on structural analysis of control and data flow of Petri nets, BPMN, UML Activity Diagrams, or EPCs) are less useful since they are not capable to handle uncertainty and imprecision.
The fuzzy theory nowadays encompasses a well organized corpus of basic notions including aggregation operations, relations, measures of information content, a calculus of fuzzy numbers. For this reason fuzzy theories are becoming a key element in the development of data and process management approaches. In the field of business process management fuzzy theory could play an important role for clustering, data analysis, data fusion, pattern recognition, modeling, multi-criteria evaluation and, more in general, for several business intelligence approaches. Fuzzy theories can be also combined with other techniques such as neural nets and evolutionary computing and applied to both business process design and management approaches as well as to complex process analysis focused on the extraction and representation of hidden knowledge.
Basing on the above consideration, the objective of the special session is to provide a forum for the discussion of recent advances in the application of fuzzy set methodology and technology to business process management problems (e.g. adaptive and context-aware process execution, process and related data representation, management of process execution, effective analytics and visualization, process mining methods, compliance, data integration and quality, process modeling) and to offer an opportunity for researchers and practitioners to identify and discuss integration advances highlighting new promising research directions.

Mario Luca Bernardi

Giustino Fortunato University

Marta Cimitile

Unitelma Sapienza University

Fabrizio Maria Maggi

University of Tartu

The constant growth of the Internet and introduction of such concepts as the Semantic Web and Linked Data create challenges as well as opportunities to transform the web into an environment providing the users with the abilities to utilize and explore it in a different – human-like – and efficient way.
The Internet is a huge collection of services, documents and different pieces of information – it is inherently heterogeneous, imprecise, uncertain, incomplete and even inconsistent. The users are not able to constantly search the web and analyze obtained data and information. There is a need for systems supporting the users in their web-related activities.
Fuzzy Logic and Soft Computing provide important and non-trivial approaches, techniques and methods ensuring a human-like way of dealing with imprecision, fusing information from multiple sources, selecting best among multiple alternatives, or representing information and knowledge using fuzzy ontology and fuzzy markup languages. It is anticipated, that applications of fuzziness and soft computing technologies to web systems will bring a new and human friendly way of interacting with the web, where many tasks related to web activities and information processing can be performed by systems on human behalf.
The special session will focus on the current research trends in the area of theory and practical aspects of intelligent systems equipped with fuzzy and other soft computing methods suitable for solving issues specific to web utilization, as well as to representation and processing of information and knowledge with special emphasis on fuzzy ontologies and their applications in various domains.

Marek Reformat

University of Alberta

Chang-Shing Lee

National University of Tainan (NUTN), Tainan, Taiwan

Trevor Martin

University of Bristol, UK

Giovanni Acampora

University of Naples Federico II, Italy

Bloody crimes among strangers or family members are becoming more and more frequent in the society, resulting in an increasing feeling of fear in the population with related sociological and relational problems. These bloody events are very critical to investigate in the absence of eye witness. Indeed, when this is not found, investigation agencies have to reconstruct the criminal actions by only depending upon the evidences found on the crime scene. Unfortunately, during their activity, investigation agencies have to cope with a strong uncertainty related to the crime scene and the evidences found in it. Indeed, a crime scene is inherently imprecise due to high interaction occurring among victim, aggressor and surrounding environment. In this scenario, finding evidences is difficult and derivations from them are not obvious but they require an expertise-based human reasoning. Moreover, the investigation activity is performed manually by means of imprecise tools. As a consequence, each step related to the investigation activity adds more and more inaccuracies. All this imprecision makes the crime investigation an application domain particularly suitable to be addressed by fuzzy logic.
The aim of this session is to provide a forum to disseminate and discuss relevant researches in the area related to the application of fuzzy logic and computational intelligence techniques to the crime investigation, in order to support the resolution of complex cases and act as a deterrent to violent crimes.

Autilia Vitiello

University of Salerno, Italy

Giovanni Acampora

University of Naples Federico II, Italy

Ciro Di Nunzio

University Magna Graecia of Catanzaro, Italy

Luciano Garofano

Italian Academy of Forensic Sciences, Italy

Maurizio Saliva

Azienda Sanitaria Locale ASL Napoli 3 Sud, Torre del Greco, Italy

Fuzzy logic influenced control and system theory under many aspects, from human-based decision making algorithms implementation to modeling and control of non-linear complex systems. Indeed, fuzzy logic techniques enable efficient and fast modelling and control of complex systems employed in industrial fields and human-shared environments where automated solutions are not allowed. Variable stiffness of the environment, deformable objects and component flexibilities are elements characterizing human-robot interaction in unstructured environments and even manufacturing hand-craft applications. The scope of this special section is to collect the state of the art of fuzzy controller applied to autonomous advanced robots that can be used in industries and in the every-days environments substituting or cooperating with humans in the execution of complex tasks where adaptive behavior is required. The main topics that will be addressed range from the classical well established applications of fault tolerance in Industrial Robotics to the control strategies for Human-Robot Interaction where standard industrial position-based control solutions are unsuitable in force-tracking or adaptive admittance/impedance applications. Other issues will be discussed such as trajectory planning for autonomous robots, modelling and identification for anthropomorphic manipulators, sensor fusion and image analysis for advanced control.

Fanny Ficuciello

University of Naples Federico II, Department of Electrical Engineering and Information Technology

Alberto Finzi

University of Naples Federico II, Department of Electrical Engineering and Information Technology

Vincenzo Calabrò

Cybernetics R&D department of Kongsberg Maritime

The emergence of Data Science is radically changing the way products and services are conceived, made, delivered and operated. Thus, the data gathering became fast-paced, leading to high volumes of data to be collected and analysed. Fuzziness is called to play a relevant role in order to make the analysis of massive data feasible, effective and meaningful. This special session aims at sharing and discussing problems and applications where fuzzy techniques are employed to build models in different industrial and manufacturing domains.

Luigi Troiano

University of Sannio

S. Irene Díaz

University of Oviedo, Spain

The volume, variety, velocity, veracity and value of data and data communication are increasing exponentially. The ''Five Vs'' are the key features of big data, and also the causes of inherent uncertainties in the representation, processing, and analysis of big data. Also, big data often contain a significant amount of unstructured, uncertain and imprecise data. Fuzzy sets, logic and systems enable us to efficiently and flexibly handle uncertainties in big data in a transparent way, thus enabling it to better satisfy the needs of real world big data applications and improve the quality of organizational data-based decisions. Successful developments in this area have appeared in many different aspects, such as fuzzy data analysis technique, fuzzy data inference methods and fuzzy machine learning. In particular, the linguistic representation and processing power of fuzzy sets is a unique tool for bridging symbolic intelligence and numerical intelligence gracefully. Hence, fuzzy techniques can help to extend machine learning in big data from the numerical data level to the knowledge rule level. It is therefore instructive and vital to gather current trends and provide a high quality forum for the theoretical research results and practical development of fuzzy techniques in handling uncertainties in big data.
This special session aims to offer a systematic overview of this new field and provides innovative approaches to handle various uncertainty issues in big data presentation, processing and analysing by applying fuzzy sets, fuzzy logic, fuzzy systems, and other computational intelligent techniques.

Jie Lu

Chin-Teng Lin



Guangquan Zhang

Farookh Khadeer Hussain

Dianshuang Wu

Vahid Behbood

Mohsen Naderpour

Social Signal Processing (SSP) is a discipline that aims at giving an accurate interpretation and appropriate display of social signals using multimodal signal processing, which are inherently characterised by high levels of imprecisions and uncertainties, by means of artificial/computational intelligence techniques. Thanks to these techniques, SSP attempts to automatically capture non verbal behavioural cues (emotions, facial expressions, prosody, gestures, postures, etc.) that accompany any human-human (and human-machine) interaction. A peculiarity of social signals is that they can take the form of complex constellations of nonverbal behavioral cues. SSP involves a multidisciplinary collaboration between human sciences (psychology, anthropology, sociology, etc.) and technology (computer vision, speech analysis and synthesis, machine learning, signal processing, etc.), making the whole process a big challenge for computational intelligence community. The main goal of this session is to collect high-quality papers where computational methods based on fuzzy reasoning are used to face this tough challenge in SSP.

Franco Cutugno

University of Naples Federico II

Antonio Origlia

University of Naples Federico II

Alessandro Vinciarelli

University of Glasgow and Idiap Research Institute

Practical applications of Affective Computing and Physiological Computing based systems seek to achieve a positive impact on our everyday lives by monitoring, recognising and acting on our physiological signals, speech, facial expressions, gestures and emotive states. Integrating these sensing modalities into intelligent and pervasive computing systems can reveal a contextually richer understanding of how emotional responses, changing moods, and sensations (pain, touch, tastes and smells) are a reaction to or influence how we interact with the environment through increasingly connected computing artifacts.
The integration of AC and PC raise new challenges for signal processing, machine learning and Computational Intelligence (CI). Fuzzy systems provide a useful methodology for addressing research challenges in AC/PC, where data sources such as: external and internal body signals (e.g. heart rate, brain waves, skin conductance and respiration), facial features, speech and human kinematics are inherently noisy and uncertain. Fuzzy systems are well suited to model and represent vague and ambiguous linguistic notions of perceptions, impulses, feelings, desires and human cognitive states which can be both subject and context dependent. As we improve ways of integrating affective and physiological data more pervasively, it will create highly complex, dynamic and information rich scenarios. Here the use of hybrid fuzzy approaches combining other CI approaches such as evolutionary algorithms and deep learning techniques can be used to create novel self-learning affective computing systems that are able to more naturally interact and empathize with people, understand their physical and emotive states and automatically respond in beneficial and useful ways.
This special session aims to bring together researchers to discuss how fuzzy logic approaches can be used to help solve challenging AC/PC problems, and develop ways of modelling and using physiological and affect information to inspire new approaches and applications.

Faiyaz Doctor

Coventry University, UK

Christian Wagner

University of Nottingham, UK

DataNova, NY, USA

Marie-Jeanne Lesot

Université Pierre et Marie Curie, France

Decision Making is a common task related to intelligent and complex activities in which human beings face situations where they must choose among different alternatives by means of reasoning and mental processes. Such decision situations usually involve different types of uncertainty according to their nature.
Dealing with uncertainty in real life has been a long term challenging task, and different methodologies and theories have been proposed in the literature to deal with it. Recently V. Torra (2010) introduced the concept of hesitant fuzzy sets (HFSs) as an extension of fuzzy sets that models the uncertainty provoked by the hesitation that might appear when it is necessary to assign the membership degree of an element to a fuzzy set. In spite of youth of this theory, it has attracted attention of leading researchers to the study of analysis of multiple operations and properties of HFSs and it has been used to solve problems in different fields such as decision making.
This special session plans to focus on the recent advances in theory of HFS and its application on decision making providing an opportunity for researchers working in these areas to discuss and share new ideas and results. We expect contributions in the use of HFS in decision making.

Ismat Beg

Lahore School of Economics, Centre for Mathematics and Statistical Sciences

Rosa M. Rodríguez

University of Granada, Spain

Zeshui Xu

Sichuan University

Rough set theory is a new mathematical approach to imperfect knowledge. Rough sets have been proposed for a very wide variety of applications. In particular, the rough set approach seems to be important for Artificial Intelligence and cognitive sciences, especially in machine learning, knowledge discovery, data mining, expert systems, approximate reasoning and pattern recognition. The objective of this special session is to showcase the real-world applications of hybridization of rough sets and fuzzy rough sets with bio-inspired optimization techniques and other methods of data exploration and approximate computation. The special emphasis will be put on hybrid solutions combining rough sets with other tools, as well as the importance of utilization of domain knowledge in the data mining and data processing solutions.
The aim of this special session is provide an opportunity for international researchers to share and review recent advances in the foundations, integration architectures, and applications of bio-inspired optimization techniques with rough set and fuzzy rough. The special session aims to solicit original, full length original articles on new findings and developments from researchers, academicians and practitioners from industries, in the area of rough set theory, fuzzy rough, granular computing, knowledge discovery and data mining.

Ahmad Taher Azar

Benha University

Valentina E. Balas

University “Aurel Vlaicu” Arad , Romania

Camelia Pintea

Universitatea Tehnica Cluj-Napoca Cluj-Napoca, Romania Join institution

Rabie A. Ramadan

Cairo University

Mario Pavone

University of Catania

Nicolaie Popescu-Bodorin

University of S-E Europe LUMINA, Bucharest, RO

The notions of near and far in mathematics can be traced back to works by F. Riesz, F. Hausdorff and V.A. Efremovich as well as later works by J.M. Smirnov, S. Leader, M. Lodato, S.A. Naimpally, J.F. Peters, A. Di Concilio, G. Di Maio, G. Beer, and many others. The related notions of resemblance and similarity can be traced back to J.H. Poincaré, who introduced sets of similar sensations (nascent tolerance classes) to represent the results of G.T. Fechner's sensation sensitivity experiments. F. Riesz introduced the concept of proximity or nearness of pairs of sets at the International Congress of Mathematicians (ICM) in 1908. Near sets have their origin in various forms of geometry and topology. Nearness of sets has led the solution of a number of problems rooted in rich mathematical structures and human perception in areas such as image processing, computer vision, object and pattern recognition. Near set theory provides a conceptual as well as practical framework that offers insights into several aspects of computer science (learning, social networks) and engineering (computer vision, pattern recognition) as well as pure and applied mathematics (proximity, topology, geometry and algebra).

Adel M. Alimi

University of Sfax, Tunisia

Fuzzy set theory is the subject of intense investigation in areas like control theory, robotics, biomedical engineering, computing with words, knowledge discovery, remote sensing and socioeconomics, to name a few. In computer vision, other fields, e.g., “machine learning”, and communities, e.g., PAMI, ICCV, CVPR, ECCV, NIPS, are arguably state-of-the-art. In particular, the majority of top performing techniques on public datasets are steeped in probability (regardless of if they are the correct theory for the task at hand) or neural networks (e.g., the deep learning revolution).
Big questions to the fuzzy set community include the following. What is (should be) the role of fuzzy set theory in computer vision? Does fuzzy set theory make the most sense and biggest impact at low-, mid- or high-level computer vision? Furthermore, do current performance measures overly favor machine learning approaches? Last, is there additional benefit that fuzzy set theory brings, and if so, how is it measured?
This special session invites new fuzzy set theory research in computer vision. It is a follow up to the 2016 WCCI tutorial Computer Vision: A Computational Intelligence Perspective and 2016 special session Fuzzy Set Theory in Computer Vision.
In particular, we encourage authors to investigate their theory using public datasets and to compare results to fuzzy and non-fuzzy methods. Topics of interest include both theory and applications in computer vision

Derek T. Anderson

Mississippi State University, USA

Chee Seng Chan

University of Malaya, Malaysia

James M. Keller

Mississippi State University, USA

This special session aims at discussing the basic principles and methods of designing intelligent interaction with the bidirectional communication based on the effective collaboration and symbiosis between the human and the artifact, i.e. robots, agents, computer and so on.
We aims at encouraging the academic and industrial discussion about the research on Human-Agent Interaction (HAI), Human-Robot Interaction (HRI), and Human-Computer Interaction (HCI) concerning Symbiotic Systems. Reflecting the fact that this society covers a wide range of topics, in this session we invite the related researchers from a variety of fields including intelligent robotics, human-machine interface, Kansei engineering and so on.

Tomohiro Yoshikawa

Nagoya University, Japan

Yoichiro Maeda

Institute of Technologists, Japan

Fuzzy interpolation provides a flexible means to perform reasoning in the presence of insufficient knowledge that is represented as sparse fuzzy rule bases. It enables approximate inferences to be carried out from a rule base that does not cover a given observation. Fuzzy interpolation also provides a way to simplify complex systems models and/or the process of fuzzy rule generation. It allows the reduction of the number of rules needed, thereby speeding up parameter optimisation and runtime efficiency.
The aim of this special session is to provide a forum: (1) to disseminate and discuss recent and significant research efforts in the development of fuzzy interpolation and related techniques, 82) to promote both theoretical and practical applications of fuzzy interpolation, and (3) to foster integration of fuzzy interpolation with other computational intelligence techniques.

Qiang Shen

Aberystwyth University, UK

Laszlo Koczy

Szechenyi Istvan University, Hungary

Shyi-Ming Chen

National Taiwan University of Science and Technology, Taiwan

Ying Li

Northwest Polytechnical University, China

Modern manufacturing environments are evolving considerably in order to adopt new ICT technologies and exploit their full potential to develop the so called factories of the future. Some of the main objectives consist on: (i) making sustainable manufacturing processes (highly efficient, productive, quality and accurate adaptive production processes), (ii) integrating human expert knowledge with the technology (iii) reducing the use of resources and generation of waste, (iv) opening new markets. The use of digital technologies throughout the manufacturing value chain plays a key role in order to achieve these goals.
Advanced Manufacturing implies an advanced degree of automation, autonomy and digitization within industrial processes and factories. Thus, advances in electronics and information technologies are considered key enabling technologies which are driving the transformation of current manufacturing systems towards the so called “Intelligent Factories”. The volume of data generated and archived in the manufacturing processes (design, simulation, monitoring, quality control, maintenance, etc.), represents a rich source of information which could potentially provide deep insight into the underlying physical processes and could also be used for process optimization. However, acquired process information is usually heterogeneous, complex, and with various degrees of uncertainty. Thus, intelligent data processing and analysis is an essential mechanism in order to extract useful knowledge models for their use in decision making.
With this special session, the organizers would like to continue the advanced manufacturing subject within FUZZ-IEEE, after its first edition introduced it in 2016 at WCCI in Canada. This time, organizers would like to give more emphasis on autonomous systems for manufacturing realized via soft computing methodologies.
The goal of this special session is to provide an insight into state of the art use of fuzzy logic based solutions in advanced manufacturing environments. These solutions should target mainly applications of: product design optimization, new manufacturing architectures for flexible manufacturing, product lifecycle management (PLM), zero-defect manufacturing, additive manufacturing, maintenance services, computer aided monitoring and quality non-destructive testing (NDT), collaborative manufacturing environments.

Luka Eciolaza

Mondragon University, Spain

George Panoutsos

University of Sheffield, UK

Ander Muniategui

Intelligent Manufacturing at IK4-LORTEK, Spain

The development of formal mathematical models to support experts in making decisions is of great importance to assure the validity of the actions derived from a decision outcome is theoretically sound. This is of special relevance in decision contexts where the information on the problem at hand is not amenable to be modelled in a quantitative and precise way. Another issue to be addressed is that of inconsistency of information and the dynamic nature of the decision making process itself. This type of decision-making is now being described as decision-making under uncertainty in inconsistent and dynamic environments.
This special session aims at gathering researchers with an interest in the research area described above. Specifically, we are interested in contributions towards the development of consensus models for such decision-making problems, as well as formal approaches that are able to support incomplete or missing information.
Contributions to this special session are expected to pay special attention to the rigorous motivation of the approaches put forward and to support all aspects of the models developed with a corresponding theoretical sound framework. Straight approaches lacking such scientific approach are discouraged.

Enrique Herrera-Viedma

University of Granada, Spain

Francisco Chiclana

De Montfort University, UK

Yucheng Dong

Sichuan University, China,

Francisco Javier Cabrerizo

University of Granada, Spain,

To enhance the robustness of the power grid, reduce greenhouse gas emission, and increase the penetration rate of renewable energy, the development of Smart Grid technologies is indispensable. Smart Grid technology uses information, communication, and automation technology to deploy an integrated power grid with smart power generation, transmission, distribution and users. Smart Grid emphasizes automation, safety, and the close cooperation between the users and suppliers to improve the operating efficiency of power system, to enhance power quality and to solidify grid reliability. From this perspective, a crucial issue is how to support the evolution of existing electrical grids from static hierarchical systems to self-organizing, high scalable and pervasive networks. Moreover, Smart Grid integrated with smart meters, EV charging stations and home (building) energy management system are the key enabling factor toward the Smart City concept.
The use of Smart Grid technology is also aligned with the policy goals of expanding the application of renewable energy, energy conservation, and carbon reduction. Renewable energies in general include wind, photovoltaic, hydroelectric, fuel cell and biomass power generation systems. They have been getting more attention recently due to cost competitiveness and environment friendly, as compared to fossil fuel and nuclear power generations. Owing to the relatively higher investment cost of renewable power generation systems, it is important to operate the systems near their maximum power output point, especially for the wind and solar PV generation systems. In addition, since the wind and solar PV power resources are intermittent, accurate predictions and modeling of wind speed and solar insolation are necessary. Plus, to have a more reliable power supply, renewable power generation systems are usually interconnected with the power grid. As a result, modeling and controlling the power grid using Smart Grid techniques, such as smart meters, micro-grids, and distribution automations become very important issues.
Additionally, due to the highly nonlinear and time-varying nature with unmodeled dynamics, effective uses of computational intelligence techniques such as fuzzy systems for the controlling and modeling of renewable power generation in a smart-grid system turn out to be very crucial for successful operations of the systems. Moreover, the large scale deployment of fuzzy-based technologies in Smart Grids could lead to a more efficient tasks distribution among the distributed energy resources and, consequently, to a sensible improvement of the electrical grid resiliency.
Hence, topics of interest of the special session would cover the whole range of researches and applications of fuzzy systems in renewable power generations and Smart Grid systems, with particular emphasis also on the emerging technologies and methodologies of Fuzzy logic and Computational Intelligence for resilient and proactive Smart Grids, ranging from methods for balancing resources to various control and security aspects. This special section not only focuses on technological breakthroughs and roadmaps in implementing the technology, but also presents the much needed sharing of best practices.

Faa-Jeng Lin

National Central University, Taiwan

Francesco Grimaccia

Polytechnic University of Milano, Italy

Marco Mussetta

Polytechnic University of Milano, Italy

Alfredo Vaccaro

University of Sannio, Italy

This special session will provide a forum to consolidate the community of researchers in the area of Complex Fuzzy Sets and Logic, share our current ideas, reflect on future directions, and communicate our ideas and vision to the larger Computational Intelligence community. As such, we welcome submissions on all aspects of complex fuzzy sets or complex fuzzy logic.

Scott Dick

University of Alberta, Canada

Sarah Greenfield

De Montfort University, UK

Francisco Chiclana

De Montfort University, UK

The European Union's Framework Programme for Research and Innovation ''Horizon 2020'' and many national research initiatives, have identified a set of challenges our societies will have to face in the middle and long term. Among them we can cite: intelligent transport, health, safe societies, tourism, disaster and crisis management, etc. In each of these challenges it is possible to recognize decision and optimization problems leading to models and frameworks (be it mathematical, linguistic, computational...) requiring suitable solution methods or algorithms. Uncertainty, vagueness, imprecision are ubiquitous in the problems associated with such challenges. Thus fuzzy sets and systems concepts, methodologies and techniques, emerge as proper tools to model and solve them.
The aim of this special session is to serve as a meeting point and discussion forum for researchers and practitioners on the latest developments on FSS methodologies for engineering applications in the context of the challenges mentioned above. Departing from problems associated with intelligent transport, health, disaster and crisis management, safe societies, smart systems, tourism, etc. we will welcome both theoretical and more application oriented contributions addressing: (1) problems related with the societal challenges, where the use of FSS allows to obtain better problem models or solutions (the role of FSS should be clearly stated); (2) description of deployed FSS-based applications for solving challenging problems; (3) description of ongoing efforts to stablish FSS based solutions; (4) FSS ''Success Stories''.

José Luis Verdegay

University of Granada, Spain

David Pelta

University of Granada, Spain

Many-valued logics have constituted for several decades key conceptual tools for the formal description and management of fuzzy, vague and uncertain information. In the last few years, the study of these logical systems has seen a bloom of new research related to the most diverse areas of mathematics and applied sciences. Relevant recent developments in this field are connected to the natural semantics of non-classical events. A nonclassical event is described by a formula in the language of a given manyvalued logic. A satisfying semantics for such events must account for their different aspects, in particular the "ontic" aspect, related to their vague nature, and the "epistemic" aspect, related to our ignorance, or approximate knowledge about them. The combination in a unique conceptual framework of the logic and the probability of a class of non-classical events, usually reached through the algebraic semantics and their topological or combinatorial dualities, provides both the theoreticians and the applicationoriented scholars with powerful tools to deal with this kind of events.
This special session is devoted to the most recent development in the realm of many-valued logics, with particular emphasis on theoretical advances related to algebraic or alternative semantics, combinatorial aspects, topological and categorical methods, proof theory and game theory, manyvalued computation. In particular, results directed towards a better understanding of the natural semantics of non-classical events will be appreciated. Further, a special attention is also given to connections and synergies between many-valued logics and other different formal approaches to vague and approximate reasoning, such as Rough Sets, Formal Concept Analysis and Relational Methods.

Pietro Codara

Institut d'Investigació en Intel.ligència Artificial (IIIA), Spain

Brunella Gerla

Università dell'Insubria, Italy

Diego Valota

Università degli Studi di Milano, Italy

Improving access and delivery of health care is a very actual topic in many countries. In order to meet the challenges posed by growing medical costs, aging population and limited resources, new technologies and new methods are being developed. Fuzzy systems play an important role in this context due to their ability to model nonlinear system behavior, to deal with non-probabilistic uncertainty and to describe model behavior in natural language, making communication with decision makers easier. This special session will bring together the contributions of prominent researchers in this area and will provide a platform to share, discuss and learn from the recent advances in the field. More specifically, the session is expected to attract papers regarding (clinical) decision support by using fuzzy set theory, process analysis with fuzzy sets, linguistic summarization of medical data and fuzzy systems-based support of healthy aging and wellbeing.

Susana M. Vieira

IDMEC, Insttituto Superior Técnico, Universidade de Lisboa

Anna Wilbik

Industrial Engineering, Eindhoven University of Technology

Ambient Intelligence has been adopted as a term referring to environments that are sensitive and responsive to the presence of people and it is a candidate to become the next wave of computing. Indeed, this novel computing approach is aimed to extend ubiquitous vision by incorporating intrinsic intelligence in pervasive systems. This idea enables the study, design and development of embodiments for smart environments that not only react to human events through sensing, interpretation and service provision, but also learn and adapt their operation and services to the users over time. These embodiments employ contextual information when available, and offer unobtrusive and intuitive interfaces to their users.
The aim of this special session is to encompass valuable research in integration of Computational Intelligence and Ambient Intelligence. This special session is aimed at sharing latest progress, current challenges and potential applications of fuzzy logic, evolutionary computation and neural and learning systems in the scenario of ambient computing environment.
This special session will be supported by all members of IEEE CIS Task Force on Ambient Intelligence. It is anticipated that members of this TF will contribute to this activity to promote the research of Computational Intelligence in the area of Ambient Intelligence. Details of this special session will be distributed publically through the task force web site.

Ahmad Lotfi

Nottingham Trent University, UK

Giovanni Acampora

University of Naples Federico II, Italy

Caroline Langensiepen

Nottingham Trent University, UK