Markov decision processes (MDPs) are a standard model for sequential decision-making
problems and are widely used across many scientific areas, including formal methods and …

How can we transform artificial intelligence (AI) capabilities into engineering systems? That
is, how can we engineer AI systems within budget constraints, certify them with respect to …

Probabilistic model checking is a formal verification technique to check whether stochastic
models satisfy properties of interest. Along with a rich theory, the community has developed …

Improved autonomy in robotic systems is needed for innovation in, eg, the marine sector.
Autonomous robots that are let loose in hazardous environments, such as underwater, need …

Different stochastic extensions of hybrid automata have been proposed in the past, with
unclear expressivity relations between them. In previous work, we related these modelling …

Clock-dependent probabilistic timed automata extend probabilistic timed automata by letting
the probabilities of discrete transitions depend on the exact values of clock variables. The …

Whereas the semantics of probabilistic languages has been extensively studied,
specification languages for their properties have received less attention—with the notable …

It is well known that Kleisli categories provide a natural language to model side effects. For
instance, in the theory of coalgebras, behavioural equivalence coincides with language …

The robustness analysis of probabilistic models has been recently the research focus
towards verifying the extent to which a system is robust against adversaries, as well as for …

Petri box calculus (PBC) is a well-known algebra of parallel processes with a Petri net
semantics. Discrete time stochastic and deterministic PBC (dtsdPBC) extends PBC with …