Quantifying the Expected Utility of Information in Multi-agent Scheduling Tasks

  • Avi Rosenfeld
  • Sarit Kraus
  • Charlie Ortiz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4676)

Abstract

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 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 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Avi Rosenfeld
    • 1
    • 2
  • Sarit Kraus
    • 2
  • Charlie Ortiz
    • 3
  1. 1.Department of Industrial Engineering, Jerusalem College of Technology, JerusalemIsrael
  2. 2.Department of Computer Science Bar Ilan University, Ramat GanIsrael
  3. 3.SRI International, 333 Ravenswood Avenue Menlo Park, CA 94025-3493USA

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