Home address: 14, Turgeneva Street, ap. N 1,
Simferopol, 95017,Crimea,
Ukraine.
Phone: (380652) 25-76-82
Office address: 4, Yaltinskaya Street,
Department of Mathematics,
Tavrical National University,
Simferopol,333007, Crimea,
Ukraine.
Phone: (380652) 23-03-25
Fax: (380652) 23-23-10
E-mail: donskoy@ccssu.crimea.ua

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1983 Received Candidate Sc. degree in Math. Cybern. from the Computer Center at the Russian Academy of Sciences, Moscow.
Diss. Title: Decision trees construction based learning algorithms.
1994 Received Doctor Sc. degree in Computer Science Theory from the Computer Center at the Russian Academy of Sciences, Moscow.
Diss. Title: Discrete decision making models with incomplete information: the synthetic approach.

Positions held

1974 - 1977 Programmer, Computer Centre at Simferopol State University.
1977 - 1983 Head of Software Develop Group, Senior scientific collaborator,Simferopol State University.
1983 - 1994 Assistant Professor, Applied MathematicDepartment, Simferopol State University.
1994 - Chief of the Chair of ComputerScience Theory, Full professor, Simferopol State University

Other details

Member of Russian Association ofArtificial Intelligence.Member of the Crimean Academy of Sciences.
Member of the Society for Mathematics,Economics and Operation Research (GMOOR),Germany.
Current research interests include Knowledge Based Systems, Pattern Recognition, Artificial Intelligence,Discrete mathematics, Optimization with Incomplete Data, Decision Support Systems,Expert Systems, AI+OR=IDSS.

Publications

Books
1. Verlan A.F.,Apatova N.V.,Donskoy V.I. Personal Computer'sLanguages. Kiev,"Naukova Dumka", 1989. 236 p. (in Russian)
2. Donskoy V.I., Bashta A.I. Discrete Decision Making Modelswith Incomplete Data. Simferopol, Tavria, 1992. 166 p.(in Russian)

Papers (more than 70)
1. Donskoy V.I. On Pattern Recognition Soft Systems Building//Programming, Moscow, 1980, 2, p.87-89. (in Russian)
2. Donskoy V.I. Decision Trees Construction based Learningalgorithms// J.Comp.Math.Math.Phys., Moscow, 1982, 22, 4,p.963-974. (in Russian)
3. Donskoy V.I Weakly defined linear boolean programmingproblems//J.Comp. Math. Math.Phys., Moscow, 1988, 28, 9,p. 1379-1385. (in Russian)
4. Donskoy V.I. A statistical criterion for deciding dynamicclassification problems//J. of Soviet Mathematics, C.B.,
New York, Vol.57, 5, 1991, p.3427-3428.
5. Donskoy V.I The Dual Expert Systems//Proceedings ofRussian Academy of Science. Tech. Cybern., 1993, 5,
p.111-119. (in Russian)
6. Donskoy V.I. Pseudo-Boolean Optimization with aDisjunctive constraint//Comp. Maths. Math. Phys., Pergamon,
1994, Vol.34, N2, p. 389-398
7. Donskoy V.I. Logical production systems: analysis andsynthesis//Cybernetics and System Analysis, Kiev, 1994,4, p.11-22. (in Russian)
8. Donskoy V.I. Expert Systems: New Applicatios//Programs,Systems, Models. Crimea Academy of Sc. Simferopol, 1995,p. 4-17. (in Russian)
9. Donskoy V.I. Intelligent Decision Making based on theCanonical Optimization Model with a Disjunctive Constraint:Theory and Applications//Proc. of Int. Conf. AIENG'96,11-13 Sep. 1996, Clearwater, Florida.10.Donskoy V., Perekhod I. Multiple Criteria Models with theLinear PseudoBoolean Functions and Disjunctive Restrictions
//Multiple Criteria Decision Making. Proc. of the 12th Int.Conf., Hagen, Germany. Springer, 1997, p.13-21.

List of proposed lectures

1. Decision making with incomplete information: knowledge based and case based optimization models (AI+OR=IDSS)
We consider optimization models based on incompleteinitial information utilization. Cases' data base and rules' knowledge base are used for the optimization models synthesis.The canonical form for these models representation is a form with disjunctive constraint. Algorithms for the disjunctive constraint synthesis using existing cases (inductive synthesis) and rules (deductive synthesis) are described. Models with incomplete definition of the goal function are considered as well.

2. Duality Expert Systems
Combined approach to Expert Systems development based on the inductive and deductive methods is expounded. The deductive method uses production rules, and inductive method uses empirical redundancies discovered by the logical decision tree learning algorithm. Both inductive and deductive decision rules are expressed in disjunctive normal form used for combined decision making, comparison, and the knowledge base and/or experimental data base edition. This so-called Dual combined approach ensures an error probability decrease and has great advantages.

3. Decision trees synthesis for Pattern Recognition and Expert Systems
Heuristics algoritms for the decision trees synthesis are considered. New probability error estimators are obtained. Logical analysis of decision trees and software realization are considered.

4. PseudoBoolean optimization with a disjunctive constraint
Algoritms for minimizing a pseudoboolean function with a constraint given in the form of a logic equation D = 1, where D is a disjunctive normal form, are considered. The different equivalent ways for representing the problems of pseudoboolean optimization, their reduction to various forms, and the complexity of their solution are investigated.

Lectures for students

1. Discrete Mathematics
2. Operation Research
3. Pattern Recognition
4. Decision Making with Incomplete Information
5. Artificial Intelligence and Expert Systems
6. Introduction in Algorithms and Computational Complexity
7. Programming