Many AI applications involve the interaction of multiple autonomous agents,\nrequiring those agents to reason about their own beliefs, as well as those of\nother agents. However, planning involving nested beliefs is known to be\ncomputationally challenging. In this work, we address the task of synthesizing\nplans that necessitate reasoning about the beliefs of other agents. We plan\nfrom the perspective of a single agent with the potential for goals and actions\nthat involve nested beliefs, non-homogeneous agents, co-present observations,\nand the ability for one agent to reason as if it were another. We formally\ncharacterize our notion of planning with nested belief, and subsequently\ndemonstrate how to automatically convert such problems into problems that\nappeal to classical planning technology for solving efficiently. Our approach\nrepresents an important step towards applying the well-established field of\nautomated planning to the challenging task of planning involving nested beliefs\nof multiple agents.\n