In the analysis of the risk associated to rare events that may lead to catastrophic
consequences with large uncertainty, it is questionable that the knowledge and information …

The notion of uncertainty is of major importance in machine learning and constitutes a key
element of machine learning methodology. In line with the statistical tradition, uncertainty …

Possibility theory stands halfway between logical and probabilistic representation
frameworks. Possibility theory, as a setting for handling epistemic uncertainty, may have a …

Foundations of Risk Analysis presents the issues core to risk analysis–understanding what
risk means, expressing risk, building risk models, addressing uncertainty, and applying …

This chapter provides an overview of possibility theory, emphasizing its historical roots and
its recent developments. Possibility theory lies at the crossroads between fuzzy sets …

The appearance of macro-engineering and mega-project have led to the increasing
complexity of modern electromechanical systems (EMSs). The complexity of the system …

Recent advances in decision making have incorporated both risk and ambiguity in decision
theory and optimization methods. These methods implement a variety of uncertainty …

Due to its major focus on knowledge representation and reasoning, artificial intelligence was
bound to deal with various frameworks for the handling of uncertainty: probability theory, but …

A generalised uncertainty quantification framework for resilience assessment of weather-
coupled, repairable power grids is presented. The framework can be used to efficiently …

Possibility theory is a computationally efficient framework for reasoning with imprecise
probabilities. Before performing any possibilistic analysis, however, the (imprecise) …