Article details

Research area
Natural language & AI

Proceedings of the Eleventh International Workshop on Cooperative Information Agents


Avi Rosenfeld, Sarit Kraus, Charles Ortiz

Quantifying the expected utility of information in multi-agent scheduling tasks


In this paper we investigate methods for analyzing the expected value of adding information in distributed task scheduling problems. As scheduling problems are NP-complete, no polynomial algorithms exist for evaluating the impact a certain constraint, or relaxing the same constraint, will have on the global problem. We present a general approach where local agents can estimate their problemĀ tightness, or how constrained their local sub-problem 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 problems will most benefit from human attention. We evaluated this approach within a distributed cTAEMS scheduling domain and found this approach was overall quite effective.

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