The use of optimization for the propagation of mixed epistemic/aleatory uncertainties is demonstrated within the context of hypersonic flows. Specifically, this work focuses on strategies applicable for models where input parameters can be divided into a set of variables containing only aleatory uncertainties and a set with epistemic uncertainties. With the input parameters divided in this way,...

We devise a sound and complete epistemic-logic axiomatization for Knightian type spaces – the generalization of Harsanyi type spaces employed in strategic games with asymmetric uncertainty/ambiguity. In a Knightian type space, each type’s epistemic attitude is represented by a set of probability measures. The axiomatization unravels how each such epistemic attitude embodies a (potentially) part...

The Belief-Desire-Intention (BDI) architecture is a practical approach for modelling large-scale intelligent systems. In the BDI setting, a complex system is represented as a network of interacting agents – or components – each one modelled based on its beliefs, desires and intentions. However, current BDI implementations are not well-suited for modelling more realistic intelligent systems whic...

Both cases involve judgment under uncertainty, with a mixture of evidence supporting and opposing each event’s likelihood. Yet, they involve what appears to be two qualitatively distinct representations of uncertainty. In the first case, Allie’s uncertainty reflects the unpredictability inherent to a stochastic process (i.e., random draws from the pool of Bingo numbers). This type of uncertaint...

Uncertainty quantification (UQ) is the process of quantitative characterization and propagation of input uncertainties to the response measure of interest in experimental and computational models. The input uncertainties in computational models can be either aleatory, i.e., irreducible inherent variations, or epistemic, i.e., reducible variability which arises from lack of knowledge. Previously...

This paper gives a broad overview of a complete framework for assessing the predictive uncertainty of scientific computing applications. The framework is complete in the sense that it treats both types of uncertainty (aleatory and epistemic) and incorporates uncertainty due to the form of the model and any numerical approximations used. Aleatory (or random) uncertainties in model inputs are tre...

This paper illustrates how interval analysis can be used as a basis for generalized models of uncertainty. When epistemic uncertainty is presented as a range and the aleatory is based on available information, or when random variables are assigned an interval probability, the uncertainty will have a Probability Bound (PB) structure. When Interval Monte Carlo (IMC) is used to sample random varia...