An approximate dynamic programming approach for semi-cooperative multi-agent resource management

Complex problems involving multiple agents exhibit varying degrees of cooperation. The levels of cooperation might reflect both differences in information as well as differences in goals. In this research, we develop a general mathematical model for distributed, semicooperative planning and suggest a solution strategy which involves decomposing the system into subproblems, each of which is specified at a certain period in time and controlled by an agent. The agents communicate marginal values of resources to each other, possibly with distortion. We design experiments to demonstrate the benefits of communication between the agents and show that, with communication, the solution quality approaches that of the ideal situation where the entire problem is controlled by a single agent

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© Sa majesté la reine, représentée par le ministre de la Défense nationale, 2006 Future military operations will still rely on increasingly complex joint and multinational operations. Thus, innovative concepts, doctrine and technologies are required to support the emergence of new planning and execution systems, ones that are more flexible, adaptive, interoperable and responsive to a changing and uncertain environment. The ability to conduct joint and multinational operations imposes shared information and systems interoperability requirements, as well as common standards to operate among coalition members. Growing global complexity and the rapid pace of current and future military operations call for a transition from the rigid vertical organizational structure of the past to the more integrated, modular and tailored decision support required by today’s demand. The recently proposed Network Centric Operations (NCO) framework offers a unique setting to take on emerging challenges. Even though recent attempts in deliberate planning tools focus on providing “on the fly ” precise tailoring and time phasing of force deployment in crisis situations, suitabl