1 School of Computing and Mathematics and Computer Science Research Institute, University of Ulster, Jordanstown, Northern Ireland Emails: {x.hong; cd.nugent; md.mulvenna}@ulster.ac.uk 2 School of Computer Science, Queen’s University Belfast, Northern Ireland Emails: {w.liu; jma03}@qub.ac.uk 3 School of Computing and Information Engineering and Computer Science Research Institute, University of...

This paper studies a pursuit-evasion problem involving a single pursuer and a single evader, where we are interested in developing a pursuit strategy that doesn’t require continuous, or even periodic, information about the position of the evader. We propose a self-triggered control strategy that allows the pursuer to sample the evader’s position autonomously, while satisfying desired performanc...

Targeted sampling is an increasingly popular method of data collection in animal-based epidemiologic studies. This sampling approach allows the user to exclusively choose samples from subpopulations that have a higher likelihood of the disease of interest. This is achieved by selecting animals from a subpopulation that exhibits some characteristic that indicates a higher probability of the pres...

Dempster Shafer theory of evidence (DS theory) and possibility theory are two main formalisms in modelling and reasoning with uncertain information. These two theories are inter-related as already observed and discussed in many papers (e.g. [DP82,DP88b]). One aspect that is common to the two theories is how to quantitatively measure the degree of conflict (or inconsistency) between pieces of un...

Situational awareness (SA) applications monitor the real world and the entities therein to support tasks such as rapid decision-making, reasoning, and analysis. Raw input about unfolding events may arrive from variety of sources in the form of sensor data, video streams, human observations, and so on, from which events of interest are extracted. Location is one of the most important attributes ...

Consider a linear program with uncertain objective coefficients, for which we have a Bayesian prior. We can collect information to improve our understanding of these coefficients, but this may be expensive, giving us a separate problem of optimizing the collection of information to improve the quality of the solution relative to the true cost coefficients. We formulate this information collecti...

Article history: Received 15 September 2010 Received in revised form 12 January 2011 Accepted 1 March 2011 Available online 6 March 2011

The Dezert–Smarandache theory (DSmT) and transferable belief model (TBM) both address concerns with the Bayesian methodology as applied to applications involving the fusion of uncertain, imprecise and conflicting information. In this paper, we revisit these concerns regarding the Bayesian methodology in the light of recent developments in the context of the DSmT and TBM. We show that, by exploi...

The interpretation of data coming from the real world may require different and often complementary uncertainty models: some are better described by possibility theory, others are intrinsically probabilistic. A logic for belief functions is introduced to axiomatize both semantics as special cases. As it properly extends classical logic, it also allows the fusion of data with different semantics...

The main goal of this work is to present the proof-theoretical and model-theoretical approach to a probabilistic logic which allows reasoning about temporal information. We extend both the language of linear time logic and the language of probabilistic logic, allowing statements like “A will always hold”and “the probability that A will hold in next moment is at least the probability that B will...