Modelling Multi-Agent Epistemic Planning in ASP
  Designing agents that reason and act upon the world has always been one of
the main objectives of the Artificial Intelligence community. While for
planning in "simple" domains the agents can solely rely on facts about the
world, in several contexts, e.g., economy, security, justice and politics, the
mere knowledge of the world could be insufficient to reach a desired goal. In
these scenarios, epistemic reasoning, i.e., reasoning about agents' beliefs
about themselves and about other agents' beliefs, is essential to design
winning strategies.
  This paper addresses the problem of reasoning in multi-agent epistemic
settings exploiting declarative programming techniques. In particular, the
paper presents an actual implementation of a multi-shot Answer Set
Programming-based planner that can reason in multi-agent epistemic settings,
called PLATO (ePistemic muLti-agent Answer seT programming sOlver). The ASP
paradigm enables a concise and elegant design of the planner, w.r.t. other
imperative implementations, facilitating the development of formal verification
of correctness.
  The paper shows how the planner, exploiting an ad-hoc epistemic state
representation and the efficiency of ASP solvers, has competitive performance
results on benchmarks collected from the literature. It is under consideration
for acceptance in TPLP.
