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Selected research areas and projects Inteligent Information Systems Group Department of Computer Science Faculty of Computer Science, Electronics and Telecomunnication,

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Presentation on theme: "Selected research areas and projects Inteligent Information Systems Group Department of Computer Science Faculty of Computer Science, Electronics and Telecomunnication,"— Presentation transcript:

1 Selected research areas and projects Inteligent Information Systems Group Department of Computer Science Faculty of Computer Science, Electronics and Telecomunnication, AGH Grzegorz Dobrowolski grzela@agh.edu.pl

2 The group prof. dr hab. inż. Edward Nawarecki dr inż. Sławomir Bieniasz dr inż. Aleksander Byrski dr hab. inż. Krzysztof Cetnarowicz dr hab. inż. Grzegorz Dobrowolski dr inż. Rafał Dreżewski dr inż. Marek Kisiel-Dorohinicki dr inż. Jarosław Koźlak dr inż. Robert Marcjan dr inż. Bartłomiej Śnieżyński dr inż. Wojciech Turek dr inż. Marek Valenta dr inż. Anna Zygmunt dr inż. Małgorzata Żabińska-Rakoczy mgr inż. Witold Rakoczy  About 20 members, including 2 full professors, 3 associated professors and 15 assistant professors, about 10 actively cooperating Ph.D. Students and trainees  Some names:

3 The group  Different areas of interest: software engineering, evolutionary computing, multi-agent systems, machine learning, complex networks analysis, mobile systems...  Active cooperation with academic institutions in Poland and Abroad (e.g. Cracow Institute of Technology, UTBM France, ESIGETEL France, Florida Inst. of Technology USA, George Mason University, USA...)  Active cooperation with industry and government agencies in Poland (e.g. Wasko SA, FIDO Intelligence, Gridwisetech, Polish Police, Polish Border Guard, Polish Platform for Homeland Security...).  Quasi-commercial specialization: solutions supporting homeland security investigation.

4 Criminal analysis support 4  Following the demand expressed by public security agencies, different criminal-analysis tools are constructed.  GSM and financial data visualisation and analysis (LINK and Mamut tools)  Sophisticated GUI research (touchscreen and MS Kinect based)  Pattern searching algorithms

5 Complex network analysis 5  Complex network constructed based on e.g. GSM bilings or blogosphere comments  Identification of roles in social network  Analysis of static and dynamic complex networks  Public security oriented applications

6 Social network analysis  Social Network Analysis  Identification of groups and key members  Analysis and prediction of group dynamics  Application domain: Analysis of social media (blogosphere - salon24.pl, Twitter), and data about phone calls  Different models of blogosphere (posts, comments, content/sentiment) Example: Identification of groups in blogosphere (salon24) with comment based sentiment counting model Calculated mean values for all stable groups in time slots, for C CPM parameter k=3: Example:Stability of discussed topics (post tags)

7 Mobile robots  Mobile robots laboratory founded in 2004.  Multi-robot systems research (FIRA robot soccer).  Autonomous moving robots.  Agent-based multi-robot systems.  Multi-robot simulation.

8 Robots as multi-agent system cyberspace reality Ag 1 Ag 2 Agent Agx Agent Agy negotiation

9 Agent-based computing 9  Hybrid computing systems utilizing the notion of agency  Distributed component-oriented computing platforms (AgE)  Optimization and simulation related applications  Nature-inspired computing

10 Agent learning 10  Reinforcement and supervised learning in agent-based system – comparison of selected algorithms – supervised sometimes better than reinforcement learning  Hybrid algorithms – classifier can be used to make space more compact for reinforcement learning  Sharing learned knowledge

11 Solving transportation problems using multi-agent approach Solving dynamic transportation problems - Pickup and Delivery Problem with Time Windows (PDPTW) and its extensions Traffic optimisation Definition of strategies and decision algorithms for autonomous entities Use of heuristic algorithms and local optimisation operators Classification of the situations using a set of measures (machine learning, data mining) Dynamic choice of best algorithms for given situations Traffic modelling: different traffic volume and intersection algorithms)\ Solving PDPTW without /with learning of best algorithm configuration

12 Selected research areas and projects Inteligent Information Systems Group Department of Computer Science Faculty of Computer Science, Electronics and Telecomunnication, AGH Grzegorz Dobrowolski grzela@agh.edu.pl


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