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General Information \ Department of A.I. \ Foundations of Artificial Intelligence Site Map  

Foundations of
Artificial Intelligence

Statistical Analysis
and Modelling

Game and Decision
Theory

Linguistic
Engineering

 

Department of
Artificial Intelligence

Department of
Theoretical
Foundations of
Computer Science

 

General Information

 
FOUNDATIONS
OF ARTIFICIAL INTELLIGENCE

Group Members
Mieczysław Kłopotek, Ph.D., Professor, Head of the Group
Piotr Borkowski, M.Sc.
Szymon Chojnacki, Ph.D.
Krzysztof Ciesielski, Ph.D.
Dariusz Czerski, Ph.D.
Marcin Mirończuk, M.Sc.
Roman Świniarski, Ph.D.,Professor
Marcin Sydow, Ph.D.
Krzysztof Trojanowski, Ph.D.
Sławomir Wierzchoń, Ph.D., Professor

Foreign Associates

Zbigniew Michalewicz, Ph.D., Professor,
University of Adelaide
Zbigniew Raś, Ph.D., Professor,
University of North Carolina

Research Areas
The research group is active in the field of intelligent systems in the following research areas:

knowledge discovery engineering
  • internet search engines
  • knowledge discovery from data, text and hypertext
meta-heuristics in optimization
  • optimization in dynamic environments
  • nature-inspired optimization methods.

The research group is working on massively parallel search engine to work with the Polish Internet resources in a novel way. Our specialty is systematizing online resources, and making their systematics perceivable to the user. Systematization is understood as automatic distribution of online resources into thematic groups, highlighting thematic channels in websites, labeling and categorizing documents and their groups. From the user's point of view, this translates into not only a more precise document identification - systematization enables also contextual search of both individual documents and their groups, such as channels or services, and diversification of the search engine response.
By diversification we mean variation in response, so that the user can see not only the best documents, but also the variety and thematic ambiguity, such as, for example, in the classic question regarding "game", which may either refer to playing or represent a term understandable for hunters only. Taking into account the context is important when looking for a document that is comprehensible only in the context of other documents in a particular thematic channel. For example, when asking a search engine about the tires - which is fairly common in autumn and spring seasons - we would expect to receive in response links to websites of tire manufacturers or tire shops rather than, e.g., to sites about hard work that makes us tired. Making use of the context will allow the search engine to return links to documents with contents containing the word "tire" in which the word "car" does not occur.
Systematization understood in this way will be a useful tool for many groups of users. Scientists and entrepreneurs will be able to look for potential partners or competitors in the market. On the other hand, systematizing will help them identify interesting research areas or gaps in the market that can be exploited.
A promising direction of research is the use of search engine technology and tools for acquiring knowledge from data, text and hypertext to analyze social networks.
An integral part of the outlined research is optimization. The research group focuses on the so-called meta-heuristic methods and algorithms based on evolutionary, immunological, or swarm intelligence methods. The main advantage of the methods we have developed ourselves is the diversification of the obtained solutions. Its benefits can be hardly overestimated, especially in case of optimization of a system with a large, dynamic environment. Besides the search engine technology, the above methods can find broad application in areas like optimization of chemical reactions, control of production processes, or simulation of social processes.
Both in the case studies of social networks and wide use of dynamic environments we exploit simulation tools constructed by our team (based on our theoretical studies) for the generation of synthetic data similar to real world ones.
We also conduct research in the classic areas of knowledge acquisition from data, but with in-depth analysis of the acquisition of so-called active classifiers, i.e. classifiers based on the distinguishing between the characteristics of these objects that can and that cannot be controlled, respectively.
In these and other areas of intelligent systems research we cooperate with the Polish search engine industry, foreign companies involved in the modeling market, as well as with Polish universities, such as the University of Cardinal Stefan Wyszynski, Polish-Japanese Institute of Information Technology, University of Nature and the Humanities, University of Gdansk, Wroclaw University of Technology, and foreign ones, such as Max Planck Institute of Computer Science (EU), University of North Carolina at Charlotte (USA), San Diego State University (USA) and University of Adelaide (Australia).



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