Various strategies for active learning have been proposed in the machine learning literature.
In uncertainty sampling, which is among the most popular approaches, the active learner …

This paper investigates the problem of bounding counterfactual queries from an arbitrary
collection of observational and experimental distributions and qualitative knowledge about …

This survey paper provides an overview of existing methods for building possibility
distributions. We both consider the case of qualitative possibility theory, where the scale …

Generalized Measure Theory examines the relatively new mathematical area of generalized
measure theory. The exposition unfolds systematically, beginning with preliminaries and …

We try to provide a tentative assessment of the role of fuzzy sets in decision analysis. We
discuss membership functions, aggregation operations, linguistic variables, fuzzy intervals …

Classical probability is not the only mathematical theory of uncertainty, or the most general.
Many authors have argued that probability theory is ill-equipped to model the 'epistemic' …

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 …

To take care of variability involved in the writing style of different individuals in this paper we
propose a robust scheme to segment unconstrained handwritten Bangla texts into lines …

The recent development of uncertainty theories that account for the notion of belief is linked
to the emergence, in the XXth century, of Decision Theory and Artificial Intelligence …

Effective uncertainty estimation is becoming increasingly attractive for enhancing the
reliability of neural networks. This work presents a novel approach, termed Credal-Set …