AI models for reasoning under partial knowledge: from theory to applications (1)02/17/2023
Speaker(s) : Davide PETTURITI (University of Perugia, Italy)
Thématique : Non-additive uncertainty measures and integrals emerged in AI and decision theory to overcome limitations of classical probability theory in addressing situations of partial knowledge. The term partial knowledge broadly refers to all cases where a complete probabilistic description of a problem is not available, like in presence of misspecification and ambiguity. This requires to deal with sets of probability measures or their envelopes, and paves the way to new classes of models that go beyond additivity. Nowadays, these theories consolidated in a vivid branch of AI that has several applications and poses the solution of challenging computational problems. The goal of this series of seminars is to provide a panorama on the main tools for reasoning with partial knowledge, and present some recent applications in different domains. The seminars are structured as follows.
Titre de ce séminaire : A glimpse of non-additive measures and integrals: theory and computational challenges
Résumé : In this seminar we present the main classes of models (lower/upper probabilities, belief functions, possibility measures, Choquet integrals and their generalizations), highlighting their connection to probability theory and expressive power. We also discuss interpretation and computational issues.
Détails: cliquer ici.
Lieu des séminaires:
Christophe.Marsala (at) nulllip6.fr