We propose a new approach to verification of probabilistic processes for which the model may not be available. We use a technique from Reinforcement Learning to approximate how far...
The standard symbolic, deducibility-based notions of secrecy are in general insufficient from a cryptographic point of view, especially in presence of hash functions. In this paper...
Abstract. We describe a denotational (game) semantics for a call-byvalue functional language with multiple threads of control, which may communicate values of general type on local...
Abstract. Stochastic optimization is a leading approach to model optimization problems in which there is uncertainty in the input data, whether from measurement noise or an inabili...
This paper investigates data-refinement by backward simulation for specifications whose semantics is given by partial relations. The standard model-theoretic approach is based on ...