PRESENT POSITION:   Professor of Computer Science, Institute of Applied Informatics, Wroclaw University of Technology, Poland.

• Deputy Director for Scientific Researches,
• Head of Artificial Intelligence Division

DATE and PLACE OF BIRTH: Long ago, Poland.

NATIONALITY: Polish.

MARITAL STATUS: Married, two sons (Jakub and Mateusz).

EDUCATION:

• Faculty of Electronics, Wroclaw University of Technology, Wroclaw, DSc 2000, (in Computer Science, Artificial Intelligence, habilitation),
• Institute of Engineering Cybernetics, Wroclaw University of Technology, PhD (in computer science: simulation methodology of complex systems),
• Institute of Engineering Cybernetics, Wroclaw University of Technology M.Sc. and Engineer (in Computer Science with investigation into simulation methodology of complex systems).

DISSERTATIONS:
DSc: Evolutionary Computation in Artificial Intelligence (1999).
PhD: Forecasting with models of population dynamic (1980).
Master: A model of evolutionary processes applying the preadaptive principle (1976).
My study: Wroclaw University of Technology, Faculty of Electronics.

MY MAIN FIELD OF RESEARCH AND TEACHING: Evolutionary Computations, Artificial Intelligence, Hybrid Systems.

I am the Mentor of Students Scientific Forum on AI CJANT.

Co-ordinator of Lower Silesia Festival of Science organised in Wroclaw University of Technology (2001-2003).

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My scientific and educational interests are focused on a few areas, namely:

• Evolutionary Algorithms (as optimisation techniques, efficiency, advanced operators),
• Solving real problems using biological analogy,
• Intelligent techniques in Stock Market Analysis,
• Neural network design using evolutionary paradigm,
• Machine learning,
• Expert systems,
• Hybrid systems,
• Intelligent agents.

During my study in the Institute of Engineering Cybernetics, Wroclaw University of Technology, together with my husband, under the supervision of Prof. Roman Galar, we have initiated project on evolutionary processes modelling. That project ended by defending the Master Thesis focused on application of so called preadaptive principle to model evolutionary processes. In the following years we have continued our interest in evolutionary modelling. I have focused my work on application of evolutionary ideas to forecasting substitution processes (Witold was working on methodological problems of computer simulation of evolutionary processes and investigating the role of diversity in accelerating evolutionary change). After finishing PhD theses we worked in Future Research Centre in Wroclaw, and in 1980s we were engaged in forecasting of socio-economic development. In the 1989 I have started to work in Department of Computer Science where I worked on specialised operators of evolutionary algorithms and Artificial Intelligence.

In Department of Computer Science I have lectures:

A Guide to Artificial Intelligence and Expert Systems

Dispute about Artificial Intelligence: can computer think? Based issue, and historical perspective. Procedural and declarative knowledge, knowledge representation. Deduction, abduction and induction in artificial intelligence. Forward and backward chaining. Decision tree and generation of rules from the decision tree. Production systems – ‘tentative’ and ‘irrevocable’ control strategy of inference process, ‘commutative’ versus ‘noncommutative’ systems. ‘Decomposability’ of production systems. Blackboard systems. Representation ‘STRIPS’ – backward and forward state propagation. Imprecise knowledge in expert systems: certainty factor, fuzzy logic. Constraint satisfaction problems – examples, heuristics. A guide to scene recognition and natural language understanding.

Genetic Algorithms and Their Applications

Prerequisite: Skill in programming (any programming language).

Theories of evolution (Lamarck, Darwin). An idea of evolutionary computation: an example of simulated annealing, hill-climbing and simple genetic algorithm. Stages of GA: definition of a fitness function, coding schema, genetic operators, and selection methods. Specialised operators for sequencing problems (TSP problems): CX, OX, PMX, etc. Genetic programming. Evolutionary strategies. During seminars the real applications of evolutionary algorithms are presented and discussed. Students are obligated to prepare own application (project course).

Knowledge Acquisition

Prerequisites: Finished the course “A guide to artificial intelligence and expert systems”, skill in programming.

The main problems with knowledge acquisition for expert systems. Knowledge acquisition from experts – the role of knowledge engineer, methods of interviewing. A guide to machine learning – concept learning simple algorithms: Find-S, Candidate-Elimination. Introduction to learning of decision trees: ID3 algorithm. Genetic algorithms as a tool of rules generation. Using of neural networks to machine learning tasks. Data Mining – acquiring knowledge from data bases. MineSet as an example of a tool of data mining.

Master seminar

Practical knowledge about: how to write, haw to prepare presentation, and haw to present master thesis. Discussion of main problems, solutions and results of master thesis.

For PhD students:

Intelligent techniques in real problems solving

The main aim of the lecture is to give general knowledge about the possible techniques and their applications. Lecture consists of two parts. First concerns cognition,   formulation of problems (it influence the possibilities of solutions), stimulation of creative thinking. The second part is dedicated to definitions of AI, historical development, expert systems, uncertainty, evolutionary algorithms, machine learnig (introduction, simple algorithms).

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TEACHING
About my lecture

SELECTED PUBLICATIONS
(Polish version of page, some available papers are in English)

INTERESTING LINKS

CJANT (in polish)
STUDENTS SCIENTIFIC FORUM ON ARTIFICIAL INTELLIGENCE (page in Polish)

my Department page (in Polish)

my Faculty page

My University page