What general criteria of validity can be put forward to allow humans, institutions, and machines to make inferences from uncertain premisses?
At LUCI Lab we apply the most advanced tools and techniques from quantitative and qualitative representations of uncertainty. Our results contribute to shaping the methodology of the science to come, and in doing so, put the logical mindset back at the heart of the scientific method.
Many-valued Logics
Many-valued logics provide a rich framework for modelling partial truth, vagueness, and graded belief. We investigate their proof-theoretic and semantic foundations, with particular attention to their application in AI and formal epistemology.
Bayesian and Probabilistic Reasoning
We study the foundations of Bayesian inference, the Principle of Maximum Entropy, and probabilistic logic. A current focus is the application of these frameworks to medical reasoning and evidence-based medicine.
Epistemic Logic
We develop epistemic and doxastic logics that model what agents know, believe, and are uncertain about — including logics for multi-agent settings and the interaction between knowledge and action.