For problems when decisions are taken prior to observing the realization of underlying random events, probabilistic constraints are an important modelling tool if reliability is a concern. A key concept to numerically dealing with probabilistic constraints is that of pefficient points. By adopting a dual point of view, we develop a solution framework that includes and extends various existing f...

Flexibility in agent scheduling increases the resilience of temporal plans in the face of new constraints. However, current metrics of flexibility ignore domain knowledge about how such constraints might arise in practice, e.g., due to the uncertain duration of a robot’s transition time from one location to another. Probabilistic temporal planning accounts for actions whose uncertain durations ...

We present posterior regularization, a probabilistic framework for structured, weakly supervised learning. Our framework efficiently incorporates indirect supervision via constraints on posterior distributions of probabilistic models with latent variables. Posterior regularization separates model complexity from the complexity of structural constraints it is desired to satisfy. By directly impo...

The admissible region is defined as the set of physically acceptable orbits (i.e., orbits with negative energies). Given additional constraints on orbital semi-major axis, eccentricity, etc, the admissible region is further constrained, resulting in the constrained admissible region (CAR). Based on known statistics of the measurement process, in this paper we replace hard constraints with a pro...

Real-time systems must satisfy timing constraints. In our previous work, we showed that a covert timing channel cannot be completely closed in some system configurations due to the timing constraints imposed by the RateMonotonic (RM) real-time scheduling algorithm. In this paper, we construct a probabilistic model to measure two quantities of a covert timing channel in RM based systems: channel...

Optimal design problems with probabilistic constraints, often referred to as reliabilitybased design optimization problems, have been the subject of extensive recent studies. Solution methods to date have focused more on improving efficiency rather than accuracy and the global convergence behavior of the solution. A new strategy utilizing an adaptive sequential linear programming (SLP) algorith...

We provide a dynamic programming principle for stochastic optimal control problems with expectation constraints. A weak formulation, using test functions and a probabilistic relaxation of the constraint, avoids restrictions related to a measurable selection but still implies the Hamilton-Jacobi-Bellman equation in the viscosity sense. We treat open state constraints as a special case of expecta...

We compare the application of Bayesian inference and the maximum entropy (MaxEnt) method for the analysis of flow networks, such as water, electrical and transport networks. The two methods have the advantage of allowing a probabilistic prediction of flow rates and other variables, when there is insufficient information to obtain a deterministic solution, and also allow the effects of uncertain...