A General Multi-agent Epistemic Planner Based on Higher-order Belief
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  In recent years, multi-agent epistemic planning has received attention from
both dynamic logic and planning communities. Existing implementations of
multi-agent epistemic planning are based on compilation into classical planning
and suffer from various limitations, such as generating only linear plans,
restriction to public actions, and incapability to handle disjunctive beliefs.
In this paper, we propose a general representation language for multi-agent
epistemic planning where the initial KB and the goal, the preconditions and
effects of actions can be arbitrary multi-agent epistemic formulas, and the
solution is an action tree branching on sensing results. To support efficient
reasoning in the multi-agent KD45 logic, we make use of a normal form called
alternating cover disjunctive formulas (ACDFs). We propose basic revision and
update algorithms for ACDFs. We also handle static propositional common
knowledge, which we call constraints. Based on our reasoning, revision and
update algorithms, adapting the PrAO algorithm for contingent planning from the
literature, we implemented a multi-agent epistemic planner called MEPK. Our
experimental results show the viability of our approach.
