On verifying expectations and observations of intelligent agents
This work addresses the verification of interactive systems for AI and multi-agent systems, but it is incremental as it builds on existing dynamic epistemic logic frameworks.
The paper tackles the problem of verifying intelligent agents' expectations and observations using public observation logic (POL), proving that the model checking problem for POL is PSPACE-complete and studying its syntactic fragments.
Public observation logic (POL) is a variant of dynamic epistemic logic to reason about agent expectations and agent observations. Agents have certain expectations, regarding the situation at hand, that are actuated by the relevant protocols, and they eliminate possible worlds in which their expectations do not match with their observations. In this work, we investigate the computational complexity of the model checking problem for POL and prove its PSPACE-completeness. We also study various syntactic fragments of POL. We exemplify the applicability of POL model checking in verifying different characteristics and features of an interactive system with respect to the distinct expectations and (matching) observations of the system. Finally, we provide a discussion on the implementation of the model checking algorithms.