Keynote Title : 50 years of fuzzy sets: The legacy
Didier Dubois is a Research Advisor at IRIT, the Computer Science Department of Paul Sabatier University in Toulouse, France and belongs to the French National Centre for Scientific Resarch (CNRS). He is the co-author, with Henri Prade, of two books on fuzzy sets and possibility theory, and more than 15 edited volumes on uncertain reasoning and fuzzy sets. Also with Henri Prade, he coordinated the HANDBOOK of FUZZY SETS series published by Kluwer (7 volumes, 1998-2000, 2 of which he co-edited). He has contributed about 200 technical journal papers on uncertainty theories and applications.
Since January 1, 1999, Didier Dubois has been co-Editor-in -Chief of Fuzzy Sets and Systems. He is also an Advisory Editor of the IEEE Transactions on Fuzzy Systems. He is a member of the Editorial Board of several technical journals, such as the International Journal on Approximate Reasoning, General Systems, and Information Sciences among others. He is a former president of the International Fuzzy Systems Association (1995-1997).
His topics of interest range from Artificial Intelligence to Operations Research and Decision Sciences, with emphasis on the modelling, representation and processing of imprecise and uncertain information in reasoning statistics, and, risk analysis and problem-solving tasks.
The founding paper on fuzzy sets, written by Lofti Zadeh is 50 years old. This seminal paper, sometimes ill-regarded at the time, has given rise to a huge literature, several dedicated journals, and many conferences each year. It as affected many areas of scientific research (sometimes marginally, sometimes significantly) ranging from mathematics (especially many-valued logics, topology, algebra and category theory) to engineering sciences (especially data processing, modeling, control, optimization, and uncertainty analysis).
This talk, whose content owes much to an almost 40 year old cooperation with Henri Prade, tries to organize the legacy of fuzzy sets in an orderly way, highlighting the main ideas, sometimes misunderstood, and pointing out what seem to be barren areas.
The main idea behind fuzzy sets is to replace Boolean algebra by many-valued ones, crisp membership to sets by gradual membership, drastic thresholds by soft ones, while extending the usual notions from logic, set theory, and inference. As a consequence – fuzziness is just implementing the idea of gradation to all forms of reasoning and problem-solving – degrees of membership are an abstract notion to be interpreted in practice. According to the area of application, several interpretations can be found such as degree of similarity (to a prototype in a class), degree of plausibility, or degree of preference.
The most popular part of the fuzzy set literature deals with clustering, modeling and control, where gradual transitions between classes and their use in interpolation are the basic contribution of fuzzy sets. Basically, it offers a reconciliation between logical notions such as Boolean categories and inference, and numerical modeling techniques in Engineering. A fuzzy model (e.g. Takagi-Sugeno) is typically a collection of local usual mathematical models, each defined on gradual overlapping domains, and related via an interpolation scheme similar to the one of neural nets. The bridge between neural nets and fuzzy sets leads to a useful trade-off between model accuracy and model interpretability.
Fuzziness is also often interpreted as a form of uncertainty. However, this view is sometimes based on a misunderstanding. Fuzzy sets can represent uncertainty in a gradual way because crisp sets are often used to represent an ill-known value (like in interval analysis) and fuzzy sets just introduce grades to soften boundaries of an uncertainty set. So in a fuzzy set it is the set that captures uncertainty. The underlying uncertainty theory is possibility theory, and the calculus of fuzzy intervals is a gradual extension of set-valued mathematics, contrary to the fuzzy modeling mainstream trend that promotes mathematical models in the usual sense, albeit constructed by means of fuzzy sets.
The talk will briefly situate various subfields of fuzzy sets such as fuzzy modeling fuzzy optimization, fuzzy clustering, fuzzy random variables, preference modeling, multifactorial evaluation, fuzzy differential equations, fuzzy databases, extensions of fuzzy sets (type 2, interval-valued and the like) in the light of the various interpretations of membership functions, and suggest potentially fruitful fuzzy set-inspired topics for future research.
References to the speaker’s works:
- D. Dubois Have Fuzzy Sets Anything to Do with Vagueness ? (with discussion) In : Understanding Vagueness -Logical, Philosophical and Linguistic Perspectives” (Petr Cintula, Chris Fermüller, Eds) vol. 36 of Studies in Logic, College Publications, pp. 317-346, 2012.
- D. Dubois, Lluis Godo, H. Prade, F.Esteva. An information-based discussion of vagueness. Handbook of Categorization in Cognitive Science, (Henri Cohen, Claire Lefebvre, Eds.) Chap. 40, Elsevier, 2005 pp. 892-913.
- D. Dubois, H. Prade: The three semantics of fuzzy sets. Fuzzy Sets and Systems, 90, 141-150, 1997.
- D. Dubois, H. Prade, (eds.) Fundamentals of Fuzzy Sets. The Handbooks of Fuzzy Sets Series, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2000.
- D. Dubois, H. Prade. Possibility theory, probability theory and multiple valued logics: A clarification . Annals of Mathematics and Artificial Intelligence. 32, 35-66, 2001.
- Didier Dubois, Henri Prade, Gradualness, uncertainty and bipolarity: Making sense of fuzzy sets, Fuzzy Sets and Systems, Volume 192, 1 April 2012, Pages 3-24
Keynote Title : Cloud Computing – Current Trends and Future Directions
Eleni Karatza is a Professor in the Department of Informatics at the Aristotle University of Thessaloniki, Greece. Dr. Karatza’s research interests include Computer Systems Modeling and Simulation, Performance Evaluation, Grid and Cloud Computing, Energy Efficiency in Large Scale Distributed Systems, Resource Allocation and Scheduling and Real-time Distributed Systems.
Professor Karatza has authored or co-authored over 185 technical papers and book chapters including four papers that earned best paper awards at international conferences. She is senior member of IEEE, ACM and SCS, and she served as an elected member of the Board of Directors at Large of the Society for Modeling and Simulation International (2009-2011). She has served as Program Chair and Keynote Speaker in International Conferences.
Professor Karatza is the Editor-in-Chief of the Elsevier Journal “Simulation Modeling Practice and Theory”, Area Editor of the “Journal of Systems and Software” of Elsevier, and she has been Guest Editor of Special Issues in multiple International Journals.
Cloud Computing is an emerging area of research. Due to cloud systems benefits of scalability and elasticity, enterprises and users are more and more adopting clouds to achieve high performance for their applications and for storing their data at low costs.
Because of the nature of these systems, there are important issues that must be addressed, such as: performance, resource allocation, efficient scheduling, energy conservation, reliability, protection of sensitive data, security and trust, cost, availability, quality. Effective management of cloud resources is crucial to use effectively the power of these systems and achieve high system performance.
The cloud computing paradigm can offer various types of services, such as computational resources for complex applications, web services, social networking, etc. Resource allocation and scheduling is a difficult task in clouds where there are many alternative heterogeneous computers. The scheduling algorithms must seek a way to maintain a good response time to leasing cost ratio. Furthermore, adequate data security and availability are critical issues that have to be considered along with energy-efficient solutions that are required to minimize the impact of cloud computing on the environment.
In this talk we will look at the challenges of cloud computing. We will present state-of-the-art research covering a variety of concepts on cloud computing, and we will provide future directions in the cloud computing area.Professor Vincenzo De Florio University of Antwerp and the iMinds research institute, Belgium http://win.uantwerpen.be/~vincenz/
Keynote Title : A behavioral framework for the discussion of resilience
Vincenzo De Florio is a post-doctoral researcher with the MOSAIC (formerly, PATS) research group of the University of Antwerp and the iMinds research institute. There he is responsible for the MOSAIC’ task force on adaptive-and-dependable systems, namely software, devices, and services that are built so as to sustain an agreed-upon quality-of-service and quality-of-experience despite the occurrence of potentially significant and sudden changes or failures in their infrastructures and environments.
Vincenzo De Florio received in 2013 the prestigious IBM Faculty Award for innovative research. He has published three books (one as author, two as editor) and about 130 papers. He is member of several program committees for international conferences and workshops. He is co-Editor-in-chief of IJARAS, the International Journal of Adaptive, Resilient and Autonomic Systems. Vincenzo launched and is chair of ANTIFRAGILE – the International Workshop on Computational Antifragility and Antifragile Engineering, now in its second edition. Moreover, he is co-chair of the Cloud Computing track of ANT 2015, the 6th International Conference on Ambient Systems, Networks and Technologies.
Resilience is one of those “general systems attributes” that appear to play a central role in several disciplines. Examples include ecology, business, psychology, industrial safety, microeconomics, computer networks, security, management science, cybernetics, control theory, as well as crisis and disaster management and recovery. Although common traits are retained, in each discipline resilience takes peculiar domain-specific meanings.
In this talk I introduce a behavioral model of resilience. Resilience is interpreted as the property emerging from the interaction of the behaviors exercised by a system and those of the environment it is set to operate in. The outcome of said interaction depends on both intrinsic and extrinsic factors: the systemic “traits” of the system together with its endowment—the system’s peculiar characteristics as well as its current state and requirements. I show how my behavioral model provides us with a unifying framework within which it is possible to express coherent definitions of concepts often misunderstood, including elasticity, entelechism (change tolerance), and antifragility.Professor Helder Coelho BioISI and Mind-Brain College, University of Lisbon, Portugal
Keynote Title : Challenges of Simulating Social Complexity
Helder Coelho is a full professor of the University of Lisbon (UL) in the Department of Informatics of the Faculty of Sciences, from August 1995, and retired from June 22, 2014. He is a permanent and elected member of the Portuguese Academy of Engineering (1999). ECCAI fellow (2002). Member of IFIP TEC12 (AI) and Chair of IFIP WG12.3 (Agents). Editor of the International Journal of Artificial Intelligence (Ceser Publications) and of the Progress in Artificial Intelligence (Springer). He is currently member of the Advisory Board of the Research Unit UECE from ISEG/UTL and FCT, member of the Advisory Board of EPIA/APPIA Congress, and member of the Steering Committee of MASTA and the BWSS Workshops. During 2014 he got an h-index of 20 according to Google Scholar. He is now Coordinator of the Consulting Committee of the Mind-Brain College of the University of Lisbon.
Recent research into “Conflict, Protest and Violence”, the development of designing and building simulation models, makes light about two important issues, the one on methodologies (the process of constructing simulations) and another on types of mechanisms (considering options for actions).
Along this speech I shall focus upon why mechanisms deserve our attention, in order to understand fully the context, the relationships and the influences, i.e. what exists in the background of those social phenomena associated to groups and individual behaviors.
Director, Institute for Analytics and Data Science (IADS),University of Essex, United Kingdom
Keynote Title : Modeling complex systems through agent-based and multi-agent systems
Maria Fasli is a Professor and currently the Director of the Institute for Analytics and Data Science at the University of Essex, United Kingdom. Between 2009 and 2014 she served as Head of the School of Computer Science and Electronic Engineering. She obtained her PhD in Computer Science in 2000, investigating logics for intelligent systems. Her research interests lie in intelligent and adaptive systems, agents and multi-agent systems (their theoretical foundations and practical applications), data exploration, analysing and modelling complex data (structured and unstructured) and Big data. She has published over 100 refereed articles in major journals, conferences and workshops in these areas. She has been involved in organising/chairing and reviewing for international conferences and specialist workshops. In 2005, she was awarded a National Teaching Fellowship by the Higher Education Academy (UK) for her innovative approaches to education and supporting the student experience. She is the author of the book “Agent Technology for E-commerce” (John Wiley and Sons, 2007).
Complex systems typically feature multiple entities which function in order to achieve their own design objectives and goals. The overall system behaviour is dependent on the interactions of its autonomous constituent parts, but there is lack of centralised control and individual components may behave in unforeseen ways and even maliciously either intentionally or not. Inevitably, the behaviour of such systems is dynamic and unpredictable in nature. To understand behaviour in this class of systems, agent-based and multi-agent systems offer a powerful tool enabling us to design such systems as societies of agents endowed with capabilities and their own goals, continuously interacting within the environment and affecting each other.
This talk will discuss some of the challenges and issues around the design of complex systems as multi-agent systems. I will provide examples of such systems in which the agents’ behaviour is guided by economic principles and examples where agents are modelled as having socio-cognitive characteristics such as beliefs and notions of trust. I will demonstrate how such systems can be used to emulate real systems and can help us understand the underlying drivers of both individual and global behaviour and emerging phenomena.