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Multi-agent planning (MAP) approaches have been typically conceived forindependent or loosely-coupled problems to enhance the benefits of distributedplanning between autonomous agents as solving this type of problems requireless coordination between the agents' sub-plans. However, when it comes totightly-coupled agents' tasks, MAP has been relegated in favour of centralizedapproaches and little work has been done in this direction. In this paper, wepresent a general-purpose MAP capable to efficiently handle planning problemswith any level of coupling between agents. We propose a cooperative refinementplanning approach, built upon the partial-order planning paradigm, that allowsagents to work with incomplete information and to have incomplete views of theworld, i.e. being ignorant of other agents' information, as well as maintainingtheir own private information. We show various experiments to compare theperformance of our system with a distributed CSP-based MAP approach over asuite of problems.

An approach to multi-agent planning with incomplete information