  Epistemic planning --- planning with knowledge and belief --- is essential in
many multi-agent and human-agent interaction domains. Most state-of-the-art
epistemic planners solve this problem by compiling to propositional classical
planning, for example, generating all possible knowledge atoms, or compiling
epistemic formula to normal forms. However, these methods become
computationally infeasible as problems grow. In this paper, we decompose
epistemic planning by delegating reasoning about epistemic formula to an
external solver. We do this by modelling the problem using \emph{functional
STRIPS}, which is more expressive than standard STRIPS and supports the use of
external, black-box functions within action models. Exploiting recent work that
demonstrates the relationship between what an agent `sees' and what it knows,
we allow modellers to provide new implementations of externals functions. These
define what agents see in their environment, allowing new epistemic logics to
be defined without changing the planner. As a result, it increases the
capability and flexibility of the epistemic model itself, and avoids the
exponential pre-compilation step. We ran evaluations on well-known epistemic
planning benchmarks to compare with an existing state-of-the-art planner, and
on new scenarios based on different external functions. The results show that
our planner scales significantly better than the state-of-the-art planner
against which we compared, and can express problems more succinctly.
