We launch the new probabilistic model checker S torm. It features the analysis of discrete-
and continuous-time variants of both Markov chains and MDPs. It supports the P rism and …

Tactile Internet with Human-in-the-Loop describes the change from the current Internet,
which focuses on the democratization of information independent of location or time, to the …

Recent advances have shown how decision trees are apt data structures for concisely
representing strategies (or controllers) satisfying various objectives. Moreover, they also …

Strategies for partially observable Markov decision processes (POMDP) typically require
memory. One way to represent this memory is via automata. We present a method to learn …

Decision tree learning is a popular classification technique most commonly used in machine
learning applications. Recent work has shown that decision trees can be used to represent …

For hybrid Markov decision processes, Stratego can compute strategies that are safe for a
given safety property and (in the limit) optimal for a given cost function. Unfortunately, these …

Strategies synthesized using formal methods can be complex and often require infinite
memory, which does not correspond to the expected behavior when trying to model Multi …

In game theory, as well as in the semantics of game logics, a strategy can be represented by
any function from states of the game to the agent's actions. That makes sense from the …

Stochastic games are a convenient formalism for modelling systems that comprise rational
agents competing or collaborating within uncertain environments. Probabilistic model …

Precondition inference is an important problem with many applications. Existing
precondition inference techniques for programs with arrays have limited ability to find and …