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

What you get is what you see: Decomposing Epistemic Planning using Functional STRIPS