  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.
