  Over the last few years, the concept of Artificial Intelligence has become
central in different tasks concerning both our daily life and several working
scenarios. Among these tasks automated planning has always been central in the
AI research community. In particular, this manuscript is focused on a
specialized kind of planning known as Multi-agent Epistemic Planning (MEP).
Epistemic Planning (EP) refers to an automated planning setting where the agent
reasons in the space of knowledge/beliefs states and tries to find a plan to
reach a desirable state from a starting one. Its general form, the MEP problem,
involves multiple agents who need to reason about both the state of the world
and the information flows between agents. To tackle the MEP problem several
tools have been developed and, while the diversity of approaches has led to a
deeper understanding of the problem space, each proposed tool lacks some
abilities and does not allow for a comprehensive investigation of the
information flows. That is why, the objective of our work is to formalize an
environment where a complete characterization of the agents' knowledge/beliefs
interaction and update is possible. In particular, we aim to achieve such goal
by defining a new action-based language for multi-agent epistemic planning and
to implement an epistemic planner based on it. This solver should provide a
tool flexible enough to reason on different domains, e.g., economy, security,
justice and politics, where considering others' knowledge/beliefs could lead to
winning strategies.
