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Decision making in multi-agent settings is a complex exercise where agents have to handle incomplete knowledge of the complete problem. Agents are interdependent in multi-agent decision making, being subject to the decisions of other agents who bring to bear other qualitative and quantitative criteria. Some aspects of this problem have been addressed in the Distributed Constraint Optimisation Problems (DCOP) and Markov Decision Processes literature. Taking inspiration from a medical example, our objective in this paper is to provide a framework to support multi-agent decision coordination. This method can be applied in scenarios where we seek to combine qualitative preferences on projected final states with assessment made using utility/objective functions, while accounting for partial agent knowledge. ....

Distributed Constraint Optimisation Problems , Markov Decision Processes , Adversarial Search , Ecision Coordination , Decision Optimisation ,