Selected research areas and projects
Inteligent Information Systems Group
Department of Computer ScienceFaculty of Computer Science, Electronics and
Telecomunnication, AGH
Grzegorz Dobrowolski [email protected]
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:
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.
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
Complex network analysis
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Number Organizer Isolator Communicator Watchman Extender Monitor Liaison Soldier Recruit Outsider Accidental
999585756 80 90 60 50 30 50 10 70 50 80 70
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351937604 80 110 80 90 10 90 10 90 60 100 80
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381814228 90 40 30 50 40 60 10 60 20 70 70
993299413 90 100 90 80 20 90 10 80 50 90 70
998201864 80 90 80 30 50 60 10 40 20 50 40
995656749 80 90 60 50 40 50 10 70 50 80 70
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
Social network analysisSocial 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:
salon 24.pl
Text analysis
SNA modelDynamics analysis
Grou
p ex
trac
tion
Example:Stability of discussed topics (post tags)
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.
Robots as multi-agent system
cyberspace
reality
Ag1
Ag2 Agent Agx
Agent Agynegotiation
Agent-based computing
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Hybrid computing systems utilizing the notion of agency
Distributed component-oriented computing platforms (AgE)
Optimization and simulation related applications
Nature-inspired computing
Agent learning
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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
Solving transportation problems using multi-agent approachSolving 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
0
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lc101 lr101 lrc101 lc201 lr201 lrc201
Com
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no learning
learning
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Average traffic volume
Ave
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loci
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km/h
]
SOTL-all
RL-all
SOTL G1-G9
SOTL G2-G3
RL G1-G9
RL G2-G3
Traffic modelling: different traffic volume and intersection algorithms)\
Solving PDPTW without/with learning of bestalgorithm configuration
Selected research areas and projects
Inteligent Information Systems Group
Department of Computer ScienceFaculty of Computer Science, Electronics and
Telecomunnication, AGH
Grzegorz Dobrowolski [email protected]
