Quantifying the Expected Utility of Information in Multi-Agent Scheduling Tasks
by Rosenfeld, A. and Kraus, S. and Ortiz, C.
Proceedings of the Eleventh International Workshop on Cooperative Information Agents, 2007.
In this paper we investigate methods for analyzing the ex- pected value of adding information in distributed task scheduling prob- lems. As scheduling problems are NP-complete, no polynomial algo- rithms exist for evaluating the impact a certain constraint, or relaxing the same constraint, will have on the global problem. We present a gen- eral approach where local agents can estimate their problem tightness, or how constrained their local subproblem is. This allows these agents to immediately identify many problems which are not constrained, and will not benefit from sending or receiving further information. Next, agents use traditional machine learning methods based on their specific local problem attributes to attempt to identify which of the constrained prob- lems will most benefit from human attention. We evaluated this ap- proach within a distributed cTAEMS scheduling domain and found this approach was overall quite effective.
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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 |  |
