Measuring the Expected Gain of Communicating Constraint Information
by Rosenfeld, A. and Kraus, S. and Ortiz, C.
Multiagent and Grid Systems, 2008.
Abstract In this paper we investigate methods for measuring the expected utility from communi- cating information in multi-agent planning and scheduling problems. We consider an envi- ronment where human teammates can potentially add information to relax constraint infor- mation. As these problems are NP-complete, no polynomial algorithms exist for evaluating the impact of either adding or relaxing a certain constraint will have on the global problem. We present a general approach based on a notion we introduce called problem tightness. Dis- tributed agents use this notion to identify those problems which are not overly constrained and, therefore, will not benefit from additional information that would relax those constraints. Finally, agents apply traditional machine learning methods based on their specific local prob- lem attributes to attempt to identify which of the constrained problems will most benefit from added information. We evaluated this approach within a distributed c-TAEMS scheduling domain and found that this approach was effective overall.
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Coordinated Multi-Agent Team Reasoning and Incremental eXecutionSRI and team members are working on developing systems that enable people working in teams to quickly and effectively manage change. |
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Ortiz, Charles L | Program Director |  |
