The complexity of planning problems comes from the size of the state graph of the systems, which suggests to consider factored (or distributed) solutions. We previously proposed a solution of this kind which revealed ...
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The complexity of planning problems comes from the size of the state graph of the systems, which suggests to consider factored (or distributed) solutions. We previously proposed a solution of this kind which revealed to be very efficient on problems where components have a sparse interaction. This work explores a step further in this direction. The idea is to extend the celebrated turbo algorithms, extremely successful to decode large-scale sparse error correcting codes. The paper proposes an adaptation of this technique to the setting of cost-optimal factored planning, and illustrates its behavior on large randomly generated systems.
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