Speaker: 
Thomas Bolander and Nina Gierasimczuk

Title: 
Learning to Act: Qualitative Learning of Deterministic Action Models

Abstract: 
In this talk we address the problem of learnability of fully observable, universally applicable action models of dynamic epistemic logic. We introduce a framework for actions seen as sets of transitions between propositional states and we relate them to their DEL representations as action models. We introduce and discuss various properties of actions and action models, e.g., new definition of determinism and (non-)conditionality. We also show ways in which action models can be simplified without sacrificing their power. We check two basic learnability criteria: finite identifiability (conclusively inferring the appropriate action model in finite time) and identifiability in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while arbitrary (non-deterministic) actions require more learning power---they are identifiable in the limit. We then move on to a particular learning method, i.e., learning via update, which proceeds via restriction of a space of events within a learning-specific action model. We show how this method can be adapted to learn conditional and non-conditional action models.