  Multi-agent planning (MAP) approaches have been typically conceived for
independent or loosely-coupled problems to enhance the benefits of distributed
planning between autonomous agents as solving this type of problems require
less coordination between the agents' sub-plans. However, when it comes to
tightly-coupled agents' tasks, MAP has been relegated in favour of centralized
approaches and little work has been done in this direction. In this paper, we
present a general-purpose MAP capable to efficiently handle planning problems
with any level of coupling between agents. We propose a cooperative refinement
planning approach, built upon the partial-order planning paradigm, that allows
agents to work with incomplete information and to have incomplete views of the
world, i.e. being ignorant of other agents' information, as well as maintaining
their own private information. We show various experiments to compare the
performance of our system with a distributed CSP-based MAP approach over a
suite of problems.
