We investigate the behaviour of population models written in Stochastic Concurrent ConstraintProgramming (sCCP), a stochastic extension of Concurrent Constraint Programming. In particular,we focus on models from which we can define a semantics of sCCP both in terms of ContinuousTime Markov Chains (CTMC) and in terms of Stochastic Hybrid Systems, in which some populationsare appr...

Dynamic constraint satisfaction is a useful tool for representing and solving sequential decision problems with complete knowledge in dynamic world and particularly constrained resource allocation problems. However, when resources are unreliable, this framework becomes limited due to the stochastic outcomes of the assignments chosen. On the contrary, Markov Decision Processes (MDPs) handle stoc...

This paper describes a complete and efficient solution to the stochastic allocation and scheduling for Multi-Processor System-on-Chip (MPSoC). Given a conditional task graph characterizing a target application and a target architecture with alternative memory and computation resources, we compute an allocation and schedule minimizing the expected value of communication cost, being the communica...

Recent social networks' misinformation mitigation approaches tend to investigate how reduce by considering a whole-network statistical scale. However, unbalanced exposures among individuals urge study fair allocation of resources. Moreover, the network has random dynamics which change over time. Therefore, we introduce stochastic and non-stationary knapsack problem, apply its resolution mitigat...

The paper presents sufficient and (under an additional technical assumption) necessary conditions that verify the relevance of given input and output processes to an assumed stochastic uncertain system model subject to an uncertainty constraint. The approach is to establish the existence of an admissible probability model under which dynamics of the proposed stochastic system model are consiste...

Abstract: We consider a semi-infinite optimization problem in Banach spaces, where both the objective functional and the constraint operator are compositions of convex nonsmooth mappings and differentiable mappings. We derive necessary optimality conditions for these problems. Finally, we apply these results to nonconvex stochastic optimization problems with stochastic dominance constraints, ge...

This paper considers a robust H¥ state feedback control problem for linear uncertain systems with stochastic uncertainty. The uncertainty class considered in the paper involves uncertain multiplicative white noise perturbations which satisfy a certain variance constraint. A state feedback controller is presented which guarantees a prescribed level of disturbance attenuation for all admissible s...