Epistemic planning – planning with knowledge and belief – is essentialin 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. It is noted that the compilations are typically exponentially larger than the original problem. However, these methods become computationally infeasible as problems grow. In addition, those methods only works on propositional variables in discrete domains. In this thesis, we decompose epistemic planning by delegating epistemic logic reasoning to an external solver. We do this by modelling the problem using 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, the capability and flexibility of the epistemic model itself are increased, as our model is able to avoid exponential pre-compilation steps and handle logics from continuous domains. 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 which we compared against, and can express problems more succinctly.