In recent years, artificial intelligence has played an increasingly important role in the field of
automated control of drones. After AlphaGo used Intensive Learning to defeat the World Go …

Controllers for dynamical systems that operate in safety-critical settings must account for
stochastic disturbances. Such disturbances are often modeled as process noise in a …

Recent advances in decision making have incorporated both risk and ambiguity in decision
theory and optimization methods. These methods implement a variety of uncertainty …

Controllers for autonomous systems that operate in safety-critical settings must account for
stochastic disturbances. Such disturbances are often modeled as process noise, and …

Uncertain partially observable Markov decision processes (uPOMDPs) allow the
probabilistic transition and observation functions of standard POMDPs to belong to a so …

Quantitative formal models capture probabilistic behaviour, real-time aspects, or general
continuous dynamics. A number of tools support their automatic analysis with respect to …

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …

Quantitative verification tools compute probabilities, expected rewards, or steady-state
values for formal models of stochastic and timed systems. Exact results often cannot be …

Leveraging autonomous systems in safety-critical scenarios requires verifying their
behaviors in the presence of uncertainties and black-box components that influence the …

This work targets the development of an efficient abstraction method for formal analysis and
control synthesis of discrete-time stochastic hybrid systems (SHS) with linear dynamics. The …