A MODAL TEMPORAL LOGIC FOR REASONING ABOUT CHANGING DATABASES WITH APPLICATIONS TO NATURAL LANGUAGE QUESTION ANSWERING (ARTIFICIAL INTELLIGENCE)

A database which models a changing world must evolve in correspondence to the world. Previous work on natural language question answering systems for databases has largely ignored the issues which arise when the database is viewed as a dynamic (rather than a static) object. We investigate the question answering behaviors that become possible with the ability to represent and reason about the possible evolution of a database. These behaviors include offering to monitor for a possible future state of the database as an indirect response to a query, and directly answering questions about prior and future possibility. We apply a propositional modal temporal logic that captures possibility and temporality to represent and reason about dynamic databases, and present a sound axiomatization and proof procedure