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 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.

[1]  Milind Tambe,et al.  Taking DCOP to the real world: efficient complete solutions for distributed multi-event scheduling , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[2]  Pedro M. Domingos.  MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.

[3]  Makoto Yokoo,et al.  The Distributed Constraint Satisfaction Problem: Formalization and Algorithms , 1998, IEEE Trans. Knowl. Data Eng..

[4]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[5]  Ian Witten,et al.  Data Mining , 2000 .

[6]  Victor R. Lesser,et al.  Solving distributed constraint optimization problems using cooperative mediation , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[7]  Milind Tambe,et al.  Towards Adjustable Autonomy for the Real World , 2002, J. Artif. Intell. Res..

[8]  Rémi Monasson,et al.  Determining computational complexity from characteristic ‘phase transitions’ , 1999, Nature.

[9]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[10]  David Sarne,et al.  Estimating information value in collaborative multi-agent planning systems , 2007, AAMAS '07.

[11]  David Stuart Robertson,et al.  Dynamic and Distributed Interaction Protocols , 2004, Adaptive Agents and Multi-Agent Systems.

[12]  Victor R. Lesser,et al.  Evolution of the GPGP/TÆMS Domain-Independent Coordination Framework , 2002, AAMAS '02.

[13]  Avi Rosenfeld.  A Study of Dynamic Coordination Mechanisms , 2009, Knowl. Eng. Rev..

[14]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[15]  Victor R. Lesser,et al.  Agent interaction in distributed POMDPs and its implications on complexity , 2006, AAMAS '06.

[16]  Victor R. Lesser.  Evolution of the GPGP/TÆMS domain-independent coordination framework , 2002, AAMAS.

[17]  H. Van Dyke Parunak,et al.  Resource-aware exploration of the emergent dynamics of simulated systems , 2003, AAMAS '03.

[18]  Michael Stonebraker,et al.  The Morgan Kaufmann Series in Data Management Systems , 1999 .

[19]  Peter C. Cheeseman,et al.  Where the Really Hard Problems Are , 1991, IJCAI.