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Multi-agent planning (MAP) deals with planning systems that reason on long-term goals by multiple collaborative agents which want to maintain privacy on their knowledge. Recently, new MAP techniques have been devised to provide efficient solutions. Most approaches expand distributed searches using modified planners, where agents exchange public information. They present two drawbacks: they are planner-dependent; and incur a high communication cost. Instead, we present two algorithms whosedoi:10.1007/s10115-018-1202-1 fatcat:gmjehdfddrhp7ppsj4eu3mzd4a