Ioan Dzitac, Professor, Aurel Vlaicu University of Arad & Rector of Agora University of Oradea, Romania
Milan Stanojevic, Professor, Faculty of Organizational Sciences;
Bogdana Stanojevic, Researcher, Mathematical Institute of the Serbian Academy of Sciences and Arts
Felisa M. Cordova, Electrical Engineer, University of Santiago, Chile
From Fuzzy Logic to Soft Computing: New Paradigms in Decision Making
Professor at Aurel Vlaicu University of Arad & Rector of Agora University of Oradea, Romania;
In this tutorial we will presents the influence of fuzzy logic in soft computing paradigms and decision making methods.
Humans have a remarkable capability to reason and make decisions in an environment of imprecision, uncertainty and partiality of knowledge, truth and class membership. It is this capability that is needed to achieve human-level machine intelligence. Achievement of human-level machine intelligence is beyond the reach of existing Artificial Intelligence (AI) techniques and more of these are based on fuzzy sets theory and fuzzy logic. In this paper we will present a summary of AI problems and a survey of some new trends and new directions in Soft Computing and AI research.
In many real-world situations, the problems of decision making are subjected to some constraints, objectives and consequences that are not accurately known. After Bellman and Zadeh introduced for the first time fuzzy sets within MCDM, many researchers have been preoccupied by decision making in fuzzy environments. The fusion between MCDM and fuzzy set theory has led to a new decision theory, known today as fuzzy multi-criteria decision making (FMCDM), where we have decision-maker models that can deal with incomplete and uncertain knowledge and information. The most important thing is that, when we want to assess, judge or decide we usually use a natural language in which the words do not have a clear, definite meaning. As a result, we need fuzzy numbers to express linguistic variables, to describe the subjective judgement of a decision maker in a quantitative manner. Fuzzy numbers (FN) most often used are triangular FN, trapezoidal FN and Gaussian FN. We highlight that the concept of linguistic variable introduced by Zadeh in 1975 allows computation with words instead of numbers and thus linguistic terms defined by fuzzy sets are intensely used in problems of decision theory for modelling uncertain information.
After Atanassov introduced the concept of intuitionistic fuzzy sets, where each element is characterized by a membership function, as in fuzzy sets, as well as by a non-membership function, the interest in the study of the problems of decision making theory with the help of intuitionistic fuzzy sets has increased.
Keywords: Fuzzy logic, artificial intelligence, soft computing, natural language computation, MCDM, TOPSIS.
Author’s short biographical note:
Prof. Ioan Dzitac, Ph.D., Senior Member of IEEE (since 2011), is an information sciences professor at Aurel Vlaicu University of Arad - Romania (since 2009), Adjunct Professor at University of Chinese Academy of Sciences - Beijing, China (2013-2016) and Rector of Agora University of Oradea - Romania (2012-2016) and (2016-2020). He received B.Sc.(eq.M.Sc.) in Mathematics (1977) and Ph.D. in Information Sciences (2002) from Babes-Bolyai University of Cluj - Napoca, Romania (University Place in Top Shanghai: 101-150; Academic Ranking of World Universities in Mathematics - 2013).
His current research interests include different aspects of artificial intelligence, applications of fuzzy logic in technology and economy.
He is co-founder and A. Editor-in-Chief of an ISI SCI Expanded quoted journal (2006), International Journal of Computers Communications & Control (nominee by Elsevier for Journal Excellence Award -Scopus Awards Romania2015) and member in Editorial Board of 8 scientific journals. Also he is co-founder and General Chair of International Conference on Computers Communications and Control and he was member of the Program Committee of more than 60 international conferences.
He was an invited speaker and/or invited special sessions¡¯ organizer and chair in China (2013: Beijing, Suzhou and Chengdu, 2015: Dalian), India (2014: Madurai), Russia (2014: Moscow) and Brazil (2015: Rio), Lithuania (2015: Druskininkai).
He has published 3 books, 12 courses and materials for students, 4 conference proceedings and more than 70 scientific papers in journals and conferences proceedings.
Multi-criteria optimization: applications and some limits of its achievements
Professor, Faculty of Organizational Sciences;
Researcher, Mathematical Institute of the Serbian Academy of Sciences and Arts;
Multi-criteria optimization (MCO) offers mathematical models to decision problems. A subfield of MCO is Multi-objective Combinatorial Optimization. One part of it, the Multi-criteria Decision Analysis (MCDA), achieved a great popularity in the last decades, due to a variety of applications in solving real-life problems. MCDA includes the decision problems with a finite discrete set of possible alternatives.
Solving general MCO problems is not less needed in practice but the approaches to solve them must be more sophisticated, since they have to explore continuous or countable but virtually infinite number of feasible decisions. The fundamentals of MCO are reviewed, and some limits of its achievements are discussed.
In our opinion, the main issue of MCO methods is the gap between the points of view of Decision Maker (DM) and analyst. DM understands in detail the real life problem, while the analyst knows how to solve the mathematical problem -- that is the model of the real-life system. We still do not know how to model the understanding of the reality. There are many ways to model DM's preferences but none is universal. The standard approaches to solve MCO problems -- a priori, a posteriori and interactive -- are emphasized in the literature. The last one is highly recommended in order to overcome the gap. However, a certain boost to the a posteriori approaches is the proof that the number of the non-dominated points of multi-objective combinatorial optimization problems, for constant number of criteria, increases polynomial with the problem size. The proof will be presented together with some results of practical experiments that showed that the number of the non-dominated points is even smaller than the polynomial upper bound.
The objective functions involved in the mathematical models of real-life problems originally are non-linear. In the very beginning, only linear programming problems were solved efficiently, thus the objective functions were linearized before optimization. Even when the DM faced the problem of optimizing various ratios, such as profits/costs or outputs/employee, the scientists searched for linearization approaches. When the performance of the computers increased, the researchers turned back to non-linear problems and tried to solve them efficiently. Then, the field of fractional programming problems -- that are the nearest generalization of the linear case -- became intensively studied. A method to derive efficient solutions to a MCO problem with linear fractional objective functions is presented; and possible ways to involve the method in a priori or a posteriori approaches are discussed.
Finally, a serial multi-modal biometric system based on optimized thresholds is presented as an alternative to parallel fusion based systems. We optimize the mathematical expectation of both types of errors -- the false acceptance and false rejection.
Author’s short biographical note:
Milan Stanojevic graduated at University of Belgrade, Faculty of Organizational Sciences in 1990. He obtained doctoral degree at the same faculty in 2005. Since 1993 he works at Faculty of Organizational Sciences, in the beginning as a teaching assistant and now as professor in the area of operation research. He has published more than 50 papers in national and international journals, and conference proceedings in the field of operational research. His research interest, as well as teaching subjects, includes multi-objective optimization, combinatorial optimization and software for operational research.
Bogdana Stanojevic graduated Mathematics and Computer Science specialization at "Transilvania" University of Braov in 1995, and she obtained her doctoral degree in Mathematics in 2003 from the Romanian Academy. Currently she is researcher at Mathematical Institute of the Serbian Academy of Sciences and Arts. Her research interests include different aspects of fuzzy optimization, multiple objective optimization, fractional programming and mathematical fundamentals of computers.
Neuro-Management: Challenges and Trends
Felisa M. Cordova
Electrical Engineer at the University of Santiago of Chile;
In spite of the fact that the prefix neuro- (something) has been extensively and even over, or wrongly, applied, it cannot be wiped out when the confluence of many emergent disciplines turns around the principles of the science of biology and especially neurobiology.
Since the discovery of the neurobiological basis that rule the process of learning and memory and the explosive development of neuroscience through what has been called the era of the brain, the amount of knowledge that is being accumulated and comprehended progressively, makes unavoidable to consider the brain functioning and the knowledge about it a great and useful value.
For us, human beings, that move and live in the realm of the social transactional behaviors, it will be increasingly valuable to know better about the logic and tendencies of human behavior especially in dynamic and complex environments.
During the last time has been evident that with the advent of the concomitant era of digital technology, social media and mass communication has expanded their actions domains to be not negligible in a near future of human and machine developing technology.
It has been by virtue of the use of our brain that we are where we are now and we, not only scientists but any people, are going to start comprehending deeply and operatively the way our brain does, to allow the way our behavior makes.
This question opens a composed field of study and applications in the dominion of human management of behavior and it is a kind of symbiotic association between neurobiology and engineering, but also between psycho-neuro-physiology and behavior.
In the present field of action, the prefix neuro- comes from Neuroeconomy, a formal neuroscientific field of research who study the neurobiological basis of decision making. It has been found several specific brain centers of information processing and evaluative comparisons that are put in motion in the brain when a person is faced in front of a monetary decision.
It turns that when focusing the attention on human organization phenomena the Neuromanagement came to occupy the place for the term referring to all the growingly fields of knowledge outlined above applied to the understanding and re-engineering of workgroups.
One of the first challenges will be to keep away from the notion that neuromanagement can be understood or confused with any other already forms of neuro-coaching-like practices.
Neuromanagement needs to keep science-based to ensure a progressive development in a future that promises increasing storage of information disposed to be transformed in knowledge to take appropriate decisions.
More formal challenges are the construction of a database containing the quantitative diagnosis of brain basal and performing signatures; the construction of compatibility matrices of signature quantitative components; and the design of the specific Workgroup composition with theoretical optimal functional engagement for specific tasks and environments and put them into the test.
Trends will be the use of artificial intelligence and self-evolving entities to recognize and classify people, according to a variety of variables that are going to be unmanageable for human processing or self-enhancing procedures development.
Through the use of the World Wide Web and the present-day platforms available in the cloud for submitting and analyze data accessing to powerful processing devices, today it has allowed the construction of a new variety of data-gathering entities based on cognitive and behavioral preferences and behaviors extracted from the interactive all-senses use of technology and the IoT.
The accelerated changes that arise on the horizon impose to Neuromanagement the same challenge that to other emerging disciplines of study and application, which is to survive and be at the pulse of the time and the changing needs.
Author’s short biographical note:
Felisa M. Cordova is Electrical Engineer at the University of Santiago of Chile USACH. She obtained the Diplome d¡äEtudes Approfondis and the degree of Docteur Ing¨¦nieur at the University of Paris XI, France (1981). Now she is Director of the School of Engineering at University Finis Terrae. She was Director of the Department of Industrial Engineering at the Faculty of Engineering of USACH, she was also Academic Vice Rector at USACH. Her main research interest is in the Business Strategy and in the Operations Management, also in Knowledge Management of the Supply Chain. She has participated in several national and international research projects in the fields of Robotics, AGV and Virtual Operation Systems in underground mining. She has published many papers in conference proceedings and international journals in the area of Robotics and Production Research. She is past-president of the Chilean Association of Automatic Control ACCA (member of IFAC). She has participated in the organization of national and international Conferences (ACCA, LCA, LCR, SEPROSUL, ICPR, CESA). She is national councilor and past Vice President of the College of Engineers of Chile, she is also member of the accreditation board of AcreditaCI.