PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Quantifying the Expected Utility of Information in Multi-Agent Scheduling Tasks
Avi Rosenfeld, Sarit Kraus and Charlie Ortiz
In: Cooperative Information Agents XI, 11th International Workshop, CIA Lecture Notes in Computer Science (4676). (2007) Springer , pp. 104-118.

Abstract

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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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:3845
Deposited By:Sarit Kraus
Deposited On:25 February 2008