  Epistemic Planning (EP) refers to an automated planning setting where the
agent reasons in the space of knowledge states and tries to find a plan to
reach a desirable state from the current state. Its general form, the
Multi-agent Epistemic Planning (MEP) problem involves multiple agents who need
to reason about both the state of the world and the information flow between
agents. In a MEP problem, multiple approaches have been developed recently with
varying restrictions, such as considering only the concept of knowledge while
not allowing the idea of belief, or not allowing for ``complex" modal operators
such as those needed to handle dynamic common knowledge. While the diversity of
approaches has led to a deeper understanding of the problem space, the lack of
a standardized way to specify MEP problems independently of solution approaches
has created difficulties in comparing performance of planners, identifying
promising techniques, exploring new strategies like ensemble methods, and
making it easy for new researchers to contribute to this research area. To
address the situation, we propose a unified way of specifying EP problems - the
Epistemic Planning Domain Definition Language, E-PDDL. We show that E-PPDL can
be supported by leading MEP planners and provide corresponding parser code that
translates EP problems specified in E-PDDL into (M)EP problems that can be
handled by several planners. This work is also useful in building more general
epistemic planning environments where we envision a meta-cognitive module that
takes a planning problem in E-PDDL, identifies and assesses some of its
features, and autonomously decides which planner is the best one to solve it.
