prof. Dr. Michal Pěchouček, MSc.

Analyze the state-of-of-the-art in the field extended form game theory. Propose formal models for modeling games with high number of players’ strategies. Design robust and scalable algorithms for finding equilibrium. http://cs.felk.cvut.cz/en/people/pechouce

Analyze the state-of-of-the-art in the field extended form game theory. Propose formal models for modeling games with high number of players’ strategies. Design robust and scalable algorithms for finding equilibrium. http://cs.felk.cvut.cz/en/people/pechouce

Analyze the state-of-of-the-art in the field of game theory, especially in the field of security games. Design new methods for strategic randomization of inspection problems. http://cs.felk.cvut.cz/en/people/pechouce

Analyze the state-of-of-the-art in the field of game theory, especially in the field of security games. Design new methods for strategic randomization of inspection problems. http://cs.felk.cvut.cz/en/people/pechouce

Analyze the state-of-of-the-art in the field of UAS collision avoidance and trajectory planning. Design methods for providing collision free traffic of high density operation of UAS systems. Efficiency of resulting methods validate on multiagent simulation experiments. http://cs.felk.cvut.cz/en/people/pechouce

Analyze the state-of-of-the-art in the field of UAS collision avoidance and trajectory planning. Design methods for providing collision free traffic of high density operation of UAS systems. Efficiency of resulting methods validate on multiagent simulation experiments. http://cs.felk.cvut.cz/en/people/pechouce

Analyze the state-of-of-the-art in the field of game theory and machine learning. Design methods that can learn opponent strategies by analyzing her observation by means of selected machine learning methods. http://cs.felk.cvut.cz/en/people/pechouce

Analyze the state-of-of-the-art in the field of game theory and machine learning. Design methods that can learn opponent strategies by analyzing her observation by means of selected machine learning methods. http://cs.felk.cvut.cz/en/people/pechouce

Analyze the state-of-of-the-art in the field of multiagent simulation. Propose methods for modeling high number of sophisticated individual rational agents in complex environment so that part of the simulation is instantiated with physical running systems (such as robots or humans) http://cs.felk.cvut.cz/en/people/pechouce

Analyze the state-of-of-the-art in the field of multiagent simulation. Propose methods for modeling high number of sophisticated individual rational agents in complex environment so that part of the simulation is instantiated with physical running systems (such as robots or humans) http://cs.felk.cvut.cz/en/people/pechouce

Analyze the state-of-of-the-art in the field of domain independent multiaagent planning and propose new methods how to extend and specialize the existing planners so that they will provide robust performance in the environments with adversarial behavior. http://cs.felk.cvut.cz/en/people/pechouce

Analyze the state-of-of-the-art in the field of domain independent multiagent planning and propose new methods how to extend and specialize the existing planners so that they will provide robust performance in the environments with adversarial behavior. http://cs.felk.cvut.cz/en/people/pechouce