Ioan Dzitac, Aurel Vlaicu University of Arad & Agora University of Oradea, Romania, firstname.lastname@example.org
Florin Gheorghe Filip,Romanian Academy, Romania, email@example.com
Misu-Jan Manolescu, Agora University of Oradea, Romania, firstname.lastname@example.org
Simona Dzitac,University of Oradea, Romania, email@example.com
In 1965 Lotfi A. Zadeh published "Fuzzy
Sets", his pioneering and controversial paper, that now reaches over
101,000 citations. All Zadeh’s papers were cited over 195,000 times.
Starting from the ideas presented in that paper, Zadeh founded later the
Fuzzy Logic theory, that proved to have useful applications, from
consumer to industrial intelligent products. We are presenting general
aspects of Zadeh’s contributions to the development of Soft
and Artificial Intelligence(AI).
In accordance with Zadeh’s definition, Soft
Computing (SC) consist of computational techniques in computer science,
machine learning and some engineering disciplines, which study, model,
and analyze very complex reality: those for which more traditional
methods have unusable or inefficiently.
SC uses soft techniques, contrasting it with
classical artificial intelligence, Hard Computing (HC) techniques), and
includes: Fuzzy Logic, Neural Computing, Evolutionary Computation,
Machine Learning, and Probabilistic Reasoning.
HC is bound by a Computer Science (CS)
concept called NP-Complete, which means that there is a direct
connection between the size of a problem and the amount of resources
needed to solve that called "grand challenge problem". SC aids to
surmount NP-complete problems by using inexact methods to give useful
but inexact answers to intractable problems.
SC became a formal CS area of study in the
early 1990’s. Earlier computational approaches could model and precisely
analyze only relatively simple systems. More complex systems arising in
biology, medicine, the humanities, management sciences, and similar
fields often remained intractable to HC. It should be pointed out that
simplicity and complexity of systems are relative, and many conventional
mathematical models have been both challenging and very productive.
SC techniques resemble biological processes
more closely than traditional techniques, which are largely based on
formal logical systems, such as Boolean logic, or rely heavily on
computer-aided numerical analysis (as in finite element analysis).
SC techniques are intended to complement HC
techniques. Unlike HC schemes, which strive
for exactness and full truth, soft computing
techniques exploit the given tolerance of imprecision, partial truth,
and uncertainty for a particular problem. The inductive reasoning plays a
larger role in SC than in HC. SC and HC can be used together in certain
Soft Computing can deal with ambiguous or
noisy data and is tolerant of imprecision, uncertainty, partial truth,
and approximation. In effect, the role model for SC is the human mind.
Artificial Intelligence and Computational
Intelligence based on SC provide the background for the development of
smart management systems and decisions in case of ill-posed problems.
The goal of this special session is to bring
together researchers interested in applications of soft computing
algorithms and procedures in quantitative management and decision
making, in order to exchange ideas on problems, solutions, and to work
together in a friendly environment.
Decision making in fuzzy environments
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 (1970) fuzzy sets within multiple-criteria
decision-making (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 Lotfi A. 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
Topics of interest include, but are not limited to, the following:
- Ant colony optimization algorithms;
- Artificial intelligence methods for web mining;
- Bayesian networks and decision graphs; Computational intelligence methods for data mining;
- Decision support systems for quantitative management;
- Decision making with missing and/or uncertain data;
- Fuzzy multi-criteria decision making;
- Fuzzy and neuro-fuzzy modelling and simulation;
- Fuzzy numbers applications to decision making;
- Fuzzy-sets-based models in operation research;
- Knowledge Discovery in Databases;
- Machine learning for intelligent support of quantitative management;
- Neural networks in decision making tools;
- Smarter decisions;
- Support Vector Machine in SC applications.
Ioan Dzitac, Florin Gheorghe Filip, Misu-Jan
Manolescu, Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist
Lotfi A. Zadeh, International Journal of Computers Communications &
Control, ISSN 1841-9836, 12(6), 748-789, December 2017.