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Recent contributions in intelligent systems
Sgurev V., Yager R., Kacprzyk J., Atanassov K., Springer International Publishing, New York, NY, 2016. 390 pp. Type: Book (978-3-319414-37-9)
Date Reviewed: May 25 2017

This is a collection of the best papers from the 2012 IEEE Intelligent Systems Conference, which was held in Sofia, Bulgaria in September 2012. The average length of the papers in this volume is 20 pages. Since there are 20 papers in all, I will try to give the flavor of the collection by grouping the papers by technique and theoretical nature rather than by giving brief descriptions of each paper. This is not the order in which the papers appear in the book, but it is hoped that the reader of this review--and eventually the book--will benefit from this approach and be able to determine the degree to which she or he is interested in the whole. One or two of the papers fit into more than one spot in this arrangement and will be referred to in all appropriate spots.

A number of the papers use generalized networks (GN) as models for processes. A GN is somewhat like a Petri network except that the nodes can store several tokens. Thus, Dimitrov discusses dataflow process networks; Krawczak et al. use a GN in the optimization of a multilayer perceptron; Ribagin et al. present a GN model of the motion of the upper arm; and Shannon et al. use a GN to model academic promotion and doctoral candidature. This last paper gives a particularly transparent example of the role of multiple tokens in a network, as each individual in the academic pipeline can be regarded as a token. Stefanova-Pavlov et al. use a GN to model telehealth services. In each paper, the GN is described in detail with illustrative diagrams of the networks and tables of the transitions.

Several of the papers use fuzzy set concepts. Delepoulle et al. apply fuzzy set methods to low-level image processing. Kolemishevskaya-Gugulovska et al. apply Tagaki-Sugeno (T-S) style fuzzy rules to the control of uncertain systems--the inverted pendulum is considered as an application. Luo et al. apply T-S fuzzy models to fuzzy systems where there are switches between regimes that are the objects of control. Sofianos et al. also consider switched fuzzy systems, based on some semi-fixed models, one adaptive model, and one re-initialized adaptive model. Todorov et al. apply fuzzy controller methods to the control of a drying cycle in a small-scale freeze-drying plant. Pencheva et al. apply intuitionistic fuzzy logic to the estimation of parameters in a genetic model.

Besides the papers mentioned above that use fuzzy methods for control, two other papers consider control applications. Hara et al. present an air-fuel ratio control system that learns and is used to control fuel injection in multi-cylinder combustion engines. Vatchova et al. apply network structure models to knowledge extraction from nonlinear complex processes. The specific process considered is one of flotation of a multicomponent ore, used to separate and concentrate mineral ores.

Two papers use ant colony methods. Fidanova et al. consider the problem of placing wireless sensors with the goal of minimizing the number of sensors and their energy consumption. Vaslieva and Penev consider free search, where individuals explore the search space independently, and particle swarm optimization in many dimensions. Their techniques are applied to a number of specific examples that can be considered “difficult.” In general, these are given by functions with many local maxima or with maxima close to the edge of the domain of interest.

Georgieva et al. apply artificial neural networks that incorporate time to the modeling of biochemical processes. The paper by Pencheva et al. mentioned above also discusses artificial neural networks.

Peeva considers the optimization of a linear objective function under fuzzy constraints. Person et al. describe a multi-agent tutoring system. A prototype of the system was used at the University of Le Havre with positive responses from the students.

Two more theoretical papers complete this overview of the contents. Sgurev et al. describe how one can use specific binary Markov processes to model the functions of propositional logic. Vassilev shows how Archimedean metrics and ultrametrics can be used to generate intuitionistic fuzzy sets.

Each paper has a comprehensive bibliography. Furthermore, the details of the various models are clearly explained with useful diagrams where these would be helpful. For the person interested in current work in intelligent systems, the book provides a useful perspective. Graduate students and practitioners will find it to be a useful overview of some current work in intelligent systems.

Reviewer:  J. P. E. Hodgson Review #: CR145300 (1708-0515)
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