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• W2183248130 abstract "Multi-agent planning is a well-studied problem with applications in various areas. Due to computational constraints, existing research typically focuses either on unstructured domains with many agents, where we are content with heuristic solutions, or domains with small numbers of agents or special structure, where we can nd provably near-optimal solutions. In contrast, here we focus on provably near-optimal solutions in domains with many agents, by exploiting inuence limits. To that end, we make two key contributions: (a) an algorithm, based on Lagrangian relaxation and randomized rounding, for solving multi-agent planning problems represented as large mixed-integer programs; (b) a proof of convergence of our algorithm to a near-optimal solution." @default.
• W2183248130 created "2016-06-24" @default.
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• W2183248130 date "2012-01-01" @default.
• W2183248130 modified "2023-09-27" @default.
• W2183248130 title "Lagrangian Relaxation for Large-Scale Multi-Agent Planning (Extended Abstract)" @default.
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