Uncertain information is being taken into account in an increasing number of application fields. In the meantime, abduction has been proved a powerful tool for handling hypothetical reasoning and incomplete knowledge. Probabilistic logical models are suitable framework to handle uncertain information, last decade many probabilistic languages have proposed, as well inference learning systems the...

We develop two general approaches to characterising the manipulation of quantum states by means probabilistic protocols constrained limitations some resource theory. First, we give a necessary condition for existence physical transformation between states, obtained using recently introduced monotone based on Hilbert projective metric. In all affine theories (e.g. coherence, asymmetry, imaginari...

Probabilistic design optimization addresses the presence of uncertainty in design problems. Extensive studies on reliability-based design optimization, i.e., problems with random variables and probabilistic constraints, have focused on improving computational efficiency of estimating values for the probabilistic functions. In the presence of many probabilistic inequality constraints, computatio...

We investigate constrained optimal control problems for linear stochastic dynamical systems evolving in discrete time. We consider minimization of an expected value cost subject to probabilistic constraints. We study the convexity of a finite-horizon optimization problem in the case where the control policies are affine functions of the disturbance input. We propose an expectation-based method ...