Multiagent Expedition with Graphical Models

We investigate a class of multiagent planning problems termed multiagent expedition, where agents move around an open, unknown, partially observable, stochastic, and physical environment, in pursuit of multiple and alternative goals of different utility. Optimal planning in multiagent expedition is highly intractable. We introduce the notion of conditional optimality, decompose the task into a set of semi-independent optimization subtasks, and apply a decision-theoretic multiagent graphical model to solve each subtask optimally. A set of techniques are proposed to enhance modeling so that the resultant graphical model can be practically evaluated. Effectiveness of the framework and its scalability are demonstrated through experiments. Multiagent expedition can be characterized as decentralized partially observable Markov decision processes (Dec-POMDPs). Hence, this work contributes towards practical planning in Dec-POMDPs.

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