We propose an algorithm for assessing probabilistic timing constraints for systems including components with uncertain delays. We make a case for designing systems based on a probabilistic relaxation of such constraints, as this has the potential for resulting in lower silicon area and/or power consumption. We consider a concrete example, an MPEG decoder, for which we discuss modeling and asses...

The authors previous work on probabilistic constraint reasoning assumes the uncertainty of numerical variables within given bounds, characterized by a priori probability distributions. It propagates such knowledge through a network of constraints, reducing the uncertainty and providing a posteriori probability distributions. An inverse problem aims at estimating parameters from observed data, b...

Chance constraints are an important modeling tool in stochastic optimization, providing probabilistic guarantees that a solution “succeeds” in satisfying a given constraint. While they control the probability of “success,” they provide no control whatsoever in the event of a “failure.” That is, they do not distinguish between a slight overor under-shoot of the bounds, and more catastrophic viol...

Runid: Bashful: probabilistic camera motion with conservative threshold 1 for high precision Runid: Doc: probabilistic camera motion with conservative threshold 2 for high precision Runid: Dopey: probabilistic camera motion with relaxed constraints (threshold 1) for high recall Runid: Grumpy: probabilistic camera motion with relaxed constraints (threshold 2) for high recall Runid: Happy: optica...

In knowledge representation, one commonly distinguishes definitions of predicates from constraints. This distinction is also useful for probabilistic programming and statistical relational learning as it explains the key differences between probabilistic programming languages such as ICL, ProbLog and Prism (which are based on definitions) and statistical relational learning languages such as Ma...

An approach to language acquisition is described in which the foundational questions are about how the child acquires adult-like language processing capacities. This approach assumes that the child’s primary task is not to identify the grammar of the target language, but rather to learn to comprehend and produce utterances in the service of communicating with others. In this framework the input...

Continuous constraint reasoning assumes the uncertainty of numerical variables within given bounds and propagates such knowledge through a network of constraints, reducing the uncertainty. In some problems there is also information about the plausibility distribution of values within such bounds. However, the classical constraint framework cannot accommodate that information. This paper describ...

Probabilistic cardinality constraints stipulate lower bounds on the marginal probability of cardinality constraints in probabilistic databases. The demo shows how the computation of Armstrong PCsketches helps design teams identify lower bounds that separate meaningful from meaningless probabilistic databases in an application domain.

Probabilistic Concurrent Constraint Programming (PCCP) [3] is an extension of Concurrent Constraint Programming (CCP) [5] where probabilistic choice operators are introduced to represent the randomness or uncertain behaviour of processes. A probabilistic choice between two processes can be though of as flipping a coin : head the first process is triggered, tail it is the second. Based on this t...