Recently, feedback control systems using random dither quantizers have been proposed for linear discrete-time systems. However, the constraints imposed on state and control variables have not yet been taken into account for the design of feedback control systems with random dither quantization. Model predictive control is a kind of optimal feedback control in which control performance over a fi...

The probabilistic sentential decision diagram (PSDD) was recently introduced as a tractable representation of probability distributions that are subject to logical constraints. Meanwhile, efforts in tractable learning achieved great success inducing complex joint distributions from data without constraints, while guaranteeing efficient exact probabilistic inference; for instance by learning ari...

Probabilistic or stochastic programming is a framework for modeling optimization problems that involve uncertainty.In this paper, we focus on multi-objective linear programmingproblems in which the coefficients of constraints and the righthand side vector are fuzzy random variables. There are several methodsin the literature that convert this problem to a stochastic or<b...

Word-level alignment of bilingual text is a critical resource for a growing variety of tasks. Probabilistic models for word alignment present a fundamental trade-off between richness of captured constraints and correlations versus efficiency and tractability of inference. In this article, we use the Posterior Regularization framework (Graça, Ganchev, and Taskar 2007) to incorporate complex cons...

We address the issue of incorporating a particular yet expressive form of integrity constraints (namely, denial constraints) into probabilistic databases. To this aim, we move away from the common way of giving semantics to probabilistic databases, which relies on considering a unique interpretation of the data, and address two fundamental problems: consistency checking and query evaluation. Th...

Title of Dissertation: BayesOWL: A Probabilistic Framework for Uncertainty in Semantic Web Zhongli Ding, Doctor of Philosophy, 2005 Dissertation directed by: Yun Peng Associate Professor Department of Computer Science and Electrical Engineering University of Maryland, Baltimore County To address the difficult but important problem of modeling uncertainty in semantic web, this research takes a p...

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...

In this article, we consider a receding horizon control of discrete-time state-dependent jump linear systems, particular kind of stochastic switching systems, subject to possibly unbounded random disturbances and probabilistic state constraints. Due to a nature of the dynamical system and the constraints, we consider a one-step receding horizon. Using inverse cumulative distribution function, w...

2 Knowledge Representation and Reasoning (1p) 3 2.1 Logic and Combinatorics (1p) . . . . . . . . . . . . . . . . . . . . 4 2.1.1 Propositional Reasoning and Constraints Satisfaction (1p) 4 2.1.2 First-Order Logic and Its Restrictions (1.5p) . . . . . . . 5 2.1.3 Knowledge, Belief, Agents, and Modal Logic (0.75p) . . . 6 2.1.4 Logic Programming, Nonmonotonic Reasoning, and Preferences (0.75p) . ...

Bayesian motion control and planning is based on the idea of fusing motion objectives (constraints, goals, priors, etc) using probabilistic inference techniques in a way similar to Bayesian sensor fusing. This approach seems promising for tackling two fundamental problems in robotic control and planning: (1) Bayesian inference methods are an ideal candidate for fusing many sources of informatio...