Talk with Nina Gierasimczuk

Abstract

Successful learning can be understood as convergence to true beliefs. Can belief revision policies (as studied in Knowledge Representation) generate sensible learning methods? The same can be asked about multi-agent belief revision, where a group of agents revise their collective conjectures by a combination of belief revision and belief merge. Finally, how could computational models of learning, such as neural networks, fit in that picture? In my talk I will address those questions using a mix of methods of modal logic and formal learning theory.