PrefaceThe impact of soft computing for the progress of artificial intelligence
Section snippets
Soft computing and artificial intelligence
On 31 August 1955, J. McCarthy (Dartmouth College, New Hampshire), M.L. Minsky (Harvard University), N. Rochester (I.B.M. Corporation) and C.E. Shannon (Bell Telephone Laboratories) proposed a meeting to a group of researchers to be held in the summer of 1956 in order to provide ideas on each aspect of learning and each feature of intelligence capable of being simulated on machines. During the meeting, later known as the Dartmouth Conference, the term artificial intelligence (AI) was coined. In
The papers in the special issue
As said, the issue is composed of five papers analyzing different relations between SC and classical AI. These five contributions are briefly reviewed as follows.
In the first paper, entitled “Fuzzy sets in machine learning and data mining”, Eyke Hüllermeier deals with the interconnections between fuzzy sets theory and machine learning. A sound review on the state of the art of several fuzzy set-based machine learning areas is first presented. Then, an analytical study on some potential
Acknowledgments
Finally, as guest editors of this special issue, we should like to thank all the authors for their contributions and the referees for their outstanding cooperation. We sincerely thank Rajkumar Roy, Editor of the Applied Soft Computing journal, for providing us with the opportunity to edit this issue.
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Cited by (5)
A Kriging-assisted multiobjective evolutionary algorithm
2017, Applied Soft Computing JournalCitation Excerpt :EVOLUTIONARY Algorithms (EAs) play an important role in the framework of artificial intelligence (AI) and, in particular in Soft-Computing (SC), when dealing with multi-objective problems in real-world engineering optimization [1,2].
Applying soft computing techniques to optimise a dental milling process
2013, NeurocomputingCitation Excerpt :At the end, the conclusions are set out and some comments on future research lines are outlined. Soft computing [10,26–30] is a set of several technologies whose aim is to solve inexact and complex problems [31,32]. It investigates, simulates, and analyses very complex issues and phenomena in order to solve real-world problems [33,34].
Hybrid models in agent-based environmental decision support
2011, Applied Soft Computing JournalCitation Excerpt :Intelligent data analysis may be defined as “encompassing statistical, pattern recognition, machine learning, data abstraction and visualization tools to support the analysis of data and discovery of principles that are encoded within the data” [18]. The authors state that the principal differences between intelligent data analysis and knowledge discovery in databases is that the techniques used are those of artificial intelligence [7] rather than pure traditional statistical methods. Intelligent data analysis refers to all methods that are devoted to support the transformation of data into information exploiting the knowledge available on the domain.
Classification model using genetic algorithm with correlated BPNN based artificial intelligent system
2018, Journal of Advanced Research in Dynamical and Control SystemsSoft computing techniques for skills assessment of highly qualified personnel
2014, Advances in Intelligent Systems and Computing