A multiagent society for military transportation scheduling

We are in the process of building a proof-of-concept automated system for scheduling all the transportation for the United States military down to a low level of detail. This is a huge problem currently handled by many hundreds of people across a large number and variety of organizations. Our approach is to use a multiagent society, with each agent performing a particular role for a particular organization. Use of a common multiagent infrastructure allows easy communication between agents, both within the transportation society and with external agents generating transportation requirements. We have demonstrated the feasi-bility of this approach on several large-scale deployment scenarios. Copyright © 2000 John Wiley & Sons, Ltd.

[1]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1998 .

[2]  Michael Wooldridge,et al.  The Belief-Desire-Intention Model of Agency , 1998, ATAL.

[3]  C. A. Coello Coello,et al.  A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques , 1999, Knowledge and Information Systems.

[4]  Les Gasser,et al.  Implementing distributed AI systems using MACE , 1987 .

[5]  Kalyanmoy Deb,et al.  Time Scheduling of Transit Systems With Transfer Considerations Using Genetic Algorithms , 1998, Evolutionary Computation.

[6]  Kenneth L. Privratsky.  Slavery and the American West: The Eclipse of Manifest Destiny and the Coming of the Civil War , 1998 .

[7]  W ReynoldsCraig.  Flocks, herds and schools: A distributed behavioral model , 1987 .

[8]  Michael R. Genesereth,et al.  A Distributed and Anonymous Knowledge Sharing Approach to Software Interoperation , 1995, Int. J. Cooperative Inf. Syst..

[9]  Gerhard Weiss,et al.  Multiagent systems: a modern approach to distributed artificial intelligence , 1999 .

[10]  Per Bak,et al.  How Nature Works , 1996 .

[11]  Michael R. Genesereth,et al.  Software agents , 1994, CACM.

[12]  Randall Davis,et al.  Negotiation as a Metaphor for Distributed Problem Solving , 1988, Artificial Intelligence.

[13]  G. Syswerda,et al.  Schedule Optimization Using Genetic Algorithms , 1991 .

[14]  Peter Ross,et al.  Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder , 1998, Evolutionary Computation.

[15]  C. Bierwirth,et al.  Genetic algorithm based scheduling in a dynamic manufacturing environment , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[16]  David M. Ryan,et al.  Flight Attendant Rostering for Short-Haul Airline Operations , 1997, Oper. Res..

[17]  Jr. Philip A Bossert.  So Many, So Much, So Far, So Fast , 1997 .

[18]  Marshall Brinn,et al.  Genetic algorithms for complex, real-time scheduling , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[19]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[20]  Cristiano Castelfranchi,et al.  Proceedings of the 7th International Workshop on Intelligent Agents VII. Agent Theories Architectures and Languages , 2000 .

[21]  Roger L. Wainwright,et al.  Dynamic scheduling of computer tasks using genetic algorithms , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[22]  Edmund H. Durfee,et al.  A Formal Study of Distributed Meeting Scheduling , 1998 .

[23]  Mark S. Fox,et al.  The Design of a Coordination Language for Multi-Agent Systems , 1996, ATAL.

[24]  David Montana,et al.  Evolutionary Algorithms for Scheduling , 2002 .

[25]  David Levine,et al.  Application of a hybrid genetic algorithm to airline crew scheduling , 1996, Comput. Oper. Res..

[26]  Edmund K. Burke,et al.  Initialization Strategies and Diversity in Evolutionary Timetabling , 1998, Evolutionary Computation.

[27]  Thomas R. Gruber,et al.  Ontolingua: a mechanism to support portable ontologies , 1991 .

[28]  Jacques Desrosiers,et al.  The Operational Airline Crew Scheduling Problem , 1997, Transp. Sci..

[29]  Thomas J. Peters,et al.  Thriving on Chaos: Handbook for a Management Revolution , 1988 .

[30]  Sandip Sen,et al.  Effects of Local Information on Group Behavior , 1996, AAAI/IAAI, Vol. 2.

[31]  Dragan Savic,et al.  WATER NETWORK REHABILITATION WITH STRUCTURED MESSY GENETIC ALGORITHM , 1997 .

[32]  Daniel D. Corkill,et al.  GBB: A Generic Blackboard Development System , 1986, AAAI.

[33]  Gordon Vidaver,et al.  Scheduling and route selection for military land moves using genetic algorithms , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[34]  Sam R. Thangiah,et al.  An Adaptive Clustering Method Using a Geometric Shape for Vehicle Routing Problems with Time Windows , 1995, ICGA.

[35]  David J. Montana,et al.  Evolving control laws for a network of traffic signals , 1996 .

[36]  David M. Raup,et al.  How Nature Works: The Science of Self-Organized Criticality , 1997 .

[37]  Süleyman Tüfekci,et al.  A genetic algorithm for the talent scheduling problem , 1994, Comput. Oper. Res..

[38]  Tad Hogg,et al.  Controlling chaos in distributed systems , 1991, IEEE Trans. Syst. Man Cybern..

[39]  Leonid Sheremetov,et al.  Weiss, Gerhard. Multiagent Systems a Modern Approach to Distributed Artificial Intelligence , 2009 .

[40]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .