We deene a language for representing context-sensitive probabilistic knowledge. A knowledge base consists of a set of universally quantiied probability sentences that include context constraints,â€¦ (More)

We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representingâ€¦ (More)

In this paper we propose a framework for combining Disjunctive Logic Programming and Poole's Probabilistic Horn Abduction. We use the concept of hypothesis to specÂ ify the probability structure. Weâ€¦ (More)

We define a context-sensitive temporal probÂ ability logic for representing classes of discrete-time temporal Bayesian networks. Context constraints allow inference to be focused on only the relevantâ€¦ (More)

A realistic system for planning with unccrtaln information in partially observable domains must be able to reason about sensing actions and to condition its further actions on the sensed information.â€¦ (More)

Many planning problems are most naturally solved using an interative loop of actions. For example, a natural plan to unload a truckload of boxes is to repeatedly remove a box until the truck isâ€¦ (More)

We deene a context-sensitive temporal probability logic for representing classes of discrete-time temporal Bayesian networks. Context constraints allow inference to be focused on only the relevantâ€¦ (More)

It has been noted that single-enzyme systems can undergo strongly damped transient oscillations. In this paper, we present a nonlinear dynamics analysis of oscillations in undriven chemical systems.â€¦ (More)

Bayesian Networks We can see in Figure 3 that a plan fragment m ay be repeated many times in a CPP. We can speed up the reasoning process by abstracti ng away the "unnecessary" nodes in the BN repâ€¦ (More)