We examine the computational complexity of testing and finding small plans in prob­ abilistic planning domains with succinct rep­ resentations. We find that many problems of interest are complete for a variety of com­ plexity classes: NP, co-NP, PP, NPPP, co­ NPPP, and PSPACE. Of these, the proba­ bilistic classes PP and NPPP are likely to be of special interest in the field of uncer­ tainty in...

A direct robust adaptive control framework for non linear uncertain systems with constant linearly parame terized uncertainty and nonlinear state dependent uncer tainty is developed The proposed framework is Lyap unov based and guarantees partial asymptotic robust sta bility of the closed loop system that is asymptotic robust stability with respect to part of the closed loop system states assoc...

Using vision to measure world distances requires that both measurements and their uncer tainties can be determined and modelled The work described in this report develops the theory of computing distances using only images and an uncertainty analysis which includes both the errors in image localisation and the uncertainty in the imaging transformation We present di erent methods of estimating t...

Data uncertainty is ubiquitous in many real-world applications suchas sensor/RFID data analysis. In this paper, we investigate uncer-tain data that exhibit local correlations, that is, each uncertain ob-ject is only locally correlated with a small subset of data, whilebeing independent of others. We propose a generic framework fordealing with this kind of uncertain and local...

We examine the Differential Grammar , a representat ion designed to discr iminate which of a set of eonfusable al ternat ives is most likely in the context it occurs in. This approach is useful whereever uncer ta inty may exist about the ident i ty of a token or sequence of tokens, including in speech recognition, optical character recognition and machine t ransla t ion. In this paper our appl ...

fn thN paper Jve pre,ren( [he short-term re[iabdi~ assessment problem os a risk mormgement problem, ~t,here the uncer[amry involved IS rela(ed with [ransousslon outages and [he consequences are tneusored by the dejtctt experanented b) ioads T>vo new rnethodf are formulated and vnplemented m an academic lest system First, v e formulate the short-term re[labdl~ related twks associated wIih the po...

Bayesian Belief Networks (BBNs) are a pow­ erful formalism for reasoning under uncer­ tainty but bear some severe limitations: they require a large amount of information be­ fore any reasoning process can start, they have limited contradiction handling capabil­ ities, and their ability to provide explana­ tions for their conclusion is still controversial. There exists a class of reasoning syste...

In this paper we analyze two recent axiomatic approaches proposed by Dubois et al. [5] and by Giang and Shenoy [10] for qualita­ tive decision making where uncertainty is de­ scribed by possibility theory. Both axiom­ tizations are inspired by von Neumann and Morgenstern's system of axioms for the case of probability theory. We show that our ap­ proach naturally unifies two axiomatic sys­ tems ...

In this paper we address the uncertainty is­ sues involved in the low-level vision task of image segmentation. Researchers in com­ puter vision have worked extensively on this problem, in which the goal is to partition (or segment) an image into regions that are ho­ mogeneous or uniform in some sense. This segmentation is often utilized by some higher­ level process, such as an object recogniti...