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Ozobots EVO

The group focuses on various aspects of artificial intelligence with a special regard on intelligent decision making in single and multi-agent scenarios. Interests of members of the group include core symbolic search algorithms for multi-agent path finding (MAPF) and its generalizations towards real-life applications, automated planning for robotic agents, multi-robot planning with focus on coordination, motion planning and related problem solving in robotics by the use of logical reasoning such as propositional satisfiability (SAT), satisfiability modulo theories (SMT) and constraint satisfaction (CSP). The integration of non-local (global) reasoning and consideration of adversarial behaviour represent on-going challenges of the group. The fundamental philosophy of our research is to promote explainability and theoretical guarantees (time/space complexity, completeness, or reasoning proofs) together with every concept being developed.

The research of the group fully supports and is integrated with teaching activities within the bachelor and the master study programs of Artificial Intelligence and Knowledge Engineering at the Faculty of Information Technology (FIT), ČVUT. We provide supervision of all types of projects and theses in some of the themes from the still growing list of topics for inspiration.


Head of the group:


Doc. RNDr. Pavel Surynek, Ph.D.



Ondřej Černý

     adversarial robots

Ján Chudý

     real robots

Dominik Kněžour

     sentiment analysis

Jindřich Kuzma

     traffic simulation

Jan Lidák

     formation maintenance

Ondřej Pleticha

     traffic planning

Nestor Popov


Zdenek Šimůnek

     adversarial reasoning

Dominik Šmejkal

     decentrallied path-finding

Yana Zabrodskaya

     collision avoidance



Radka Bodnárová

     autonomous traffic

Vojtěch Cahlík

     puzzle solving

Zuzana Filová

     automated music processing

Berker Katipoglu

     makespan optimization

Róbert Selvek

     evacuation algorithms

Tomáš Vlk

     search heuristics

Martin Zukal

     factored multi-agent path finding

Research Grants

19-17966S: intALG-MAPFg: Intelligent Algorithms for Generalized Variants of Multi-agent Pathfinding, GAČR - Czech Science Foundation, standard project, 2019-2021.

SGS17/210/OHK3/3T/18: Modern data-mining methods for advanced extraction of information from data, CTU Project, 2017-2019.

Academic Cooperation

Kobe University, Japan (artificial intelligence, robot navigation, distributed CSP), BGU - Ben Gurion University of the Negev, Israel (MAPF, heuristic search), USC - University of Southern California, USA (MAPF, robotics), Waseda University, Japan (graph theory), AIRC/AIST - Artificial Intelligence Resarch Center/National Institute of Advanced Industrial Science and Technology, Tokyo, Japan (robotics, artificial intelligence), NII - National Institute of Informatics, Tokyo, Japan, AIP/Riken, Tokyo, Japan, IBM Resarch, Ireland.