Towards a Logic for Reasoning About Learning in a Changing World

We develop an algebraic modal logic that combines epistemic and dynamic modalities with a view to modelling information acquisition (learning) by automated agents in a changing world. Unlike most treatments of dynamic epistemic logic, we have transitions that “change the state” of the underlying system and not just the state of knowledge of the agents. The key novel feature that emerges is the need to have a way of “inverting transitions” and distinguishing between transitions that “really happen” and transitions that are possible. Our approach is algebraic, rather than being based on a Kripke-style semantics. The semantics are given in terms of quantales. We introduce a class of quantales with the appropriate inverse operations and use it to model toy robot-navigation problems, which illustrate how an agent learns information by taking actions. We discuss how a sound and complete logic of the algebra may be obtained from the positive fragment of PDL with converse.