Uncertain Evidence in Artificial Intelligence

Supervisor: Daira Pinto Prieto (d.pintoprieto@uva.nl)

Background

Give some background information

Developing methods for aggregating uncertain information is a thriving area of research. Some of the questions we can investigate on this topic are: What are we uncertain about? How does uncertainty interact with other properties of information (such as consistency or relevance)? How do different aggregation methods behave in practice?

Potential projects:

• Compare different evidence combination rules on a relevant dataset focusing on those cases with mutually contradictory evidence.
• A literature review about epistemic and aleatory uncertainty in artificial intelligence. This review could be complemented with illustrative examples built on real-life data and a (non-extensive) review on computational methods to deal with these kinds of uncertainty.

Literature

• Chaki, J. (2023). Handling uncertainty in artificial intelligence (1st ed.). Springer Singapore. ISBN 978-981-99-5333-2.