A logic for reasoning about ambiguity
This addresses a theoretical limitation in multi-agent systems for researchers in logic and AI, though it appears incremental as it builds on existing modal logic foundations.
The paper tackles the problem that standard multi-agent modal logic models fail to capture ambiguous information, which can be interpreted differently by agents, by proposing a framework with various semantics to model ambiguity and analyzing its expressive power compared to non-ambiguous logics.
Standard models of multi-agent modal logic do not capture the fact that information is often \emph{ambiguous}, and may be interpreted in different ways by different agents. We propose a framework that can model this, and consider different semantics that capture different assumptions about the agents' beliefs regarding whether or not there is ambiguity. We examine the expressive power of logics of ambiguity compared to logics that cannot model ambiguity, with respect to the different semantics that we propose.