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Intelligent Information Systems for Homeland Security and Multi-Agent Systems

Our research group focuses on software engineering, evolutionary computing, multi-agent systems, machine learning, and complex networks analysis. We actively collaborate with academic institutions and industry partners to develop solutions supporting homeland security investigation and criminal analysis. Additionally, we specialize in social network analysis and mobile robot systems.

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Intelligent Information Systems for Homeland Security and Multi-Agent Systems

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  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 • 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: 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

  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 • 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 4

  5. Complex network analysis • 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 5

  6. Social network analysis • 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: • 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: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 Ag2 Ag1 Agent Agx Agent Agy negotiation reality

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

  10. Agent learning • 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 10

  11. Solvingtransportationproblemsusingmulti-agentapproach Solving PDPTW without /with learning of best algorithm configuration • 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)\

  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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