Dynamic Logic of Trust-Based Beliefs
This work addresses foundational issues in AI and logic for modeling trust-based beliefs, but it is incremental as it builds on existing dynamic logic frameworks.
The paper tackles the problem of formalizing beliefs based on data and public announcements by developing a dynamic logic, resulting in a sound and complete axiomatization and a polynomial model checking algorithm.
Traditionally, an agent's beliefs would come from what the agent can see, hear, or sense. In the modern world, beliefs are often based on the data available to the agents. In this work, we investigate a dynamic logic of such beliefs that incorporates public announcements of data. The main technical contribution is a sound and complete axiomatisation of the interplay between data-informed beliefs and data announcement modalities. We also describe a non-trivial polynomial model checking algorithm for this logical system.