Pushing the Limits of Rational Agents: The Trading Agent Competition for Supply Chain Management

Over the years, competitions have been important catalysts for progress in Artificial Intelligence. We describe one such competition, the Trading Agent Competition for Supply Chain Management (TAC SCM). We discuss its significance in the context of today’s global market economy as well as AI research, the ways in which it breaks away from limiting assumptions made in prior work, and some of the advances it has engendered over the past six years. TAC SCM requires autonomous supply chain entities, modeled as agents, to coordinate their internal operations while concurrently trading in multiple dynamic and highly competitive markets. Since its introduction in 2003, the competition has attracted over 150 entries and brought together researchers from AI and beyond in the form of 75 competing teams from 25 different countries.

[1]  H. Simon,et al.  Rational Decision Making in Business Organizations , 1978 .

[2]  D. Sterman,et al.  Misperceptions of Feedback in a Dynamic Decision Making Experiment , 1989 .

[3]  Michael P. Wellman The economic approach to artificial intelligence , 1995, CSUR.

[4]  Norman Sadeh,et al.  A Blackboard Architecture for Integrating Process Planning and Production Scheduling , 1998 .

[5]  Michael P. Wellman,et al.  The Michigan Internet AuctionBot: a configurable auction server for human and software agents , 1998, AGENTS '98.

[6]  Jayashankar M. Swaminathan,et al.  Modeling Supply Chain Dynamics: A Multiagent Approach , 1998 .

[7]  M. Bensaou Portfolios of Buyer-Supplier Relationships , 1999 .

[8]  Wedad Elmaghraby,et al.  Supply Contract Competition and Sourcing Policies , 2000, Manuf. Serv. Oper. Manag..

[9]  Norman M. Sadeh,et al.  Agent-Based E-Supply Chain Decision Support , 2003, J. Organ. Comput. Electron. Commer..

[10]  Norman M. Sadeh,et al.  TAC-03 - A Supply-Chain Trading Competition , 2003, AI Mag..

[11]  D. J. Wu,et al.  Integrating Long- and Short-Term Contracting via Business-to-Business Exchanges for Capital-Intensive Industries , 2003, Manag. Sci..

[12]  Maria L. Gini,et al.  Analysis and Design of Supply-Driven Strategies in TAC SCM , 2004 .

[13]  John Collins,et al.  The Supply Chain Management Game for the 2007 Trading Agent Competition , 2004 .

[14]  Doina Precup,et al.  RedAgent-2003: an autonomous, market-based supply-chain management agent , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[15]  Michael P. Wellman,et al.  Value-driven procurement in the TAC supply chain game , 2004, SECO.

[16]  Michael Carl Tschantz,et al.  A stochastic programming approach to scheduling in TAC SCM , 2004, EC '04.

[17]  Norman M. Sadeh,et al.  The supply chain trading agent competition , 2005, Electron. Commer. Res. Appl..

[18]  Michael P. Wellman,et al.  STRATEGIC INTERACTIONS IN A SUPPLY CHAIN GAME , 2005, Comput. Intell..

[19]  Per Capita,et al.  About the authors , 1995, Machine Vision and Applications.

[20]  David Levine,et al.  CABOB: A Fast Optimal Algorithm for Winner Determination in Combinatorial Auctions , 2005, Manag. Sci..

[21]  Joakim Eriksson,et al.  Evolution of a supply chain management game for the Trading Agent Competition , 2006, AI Commun..

[22]  Norman M. Sadeh,et al.  CMieux: adaptive strategies for competitive supply chain trading , 2006, ICEC '06.

[23]  Norman M. Sadeh,et al.  Pricing for customers with probabilistic valuations as a continuous knapsack problem , 2006, ICEC '06.

[24]  Nicholas R. Jennings,et al.  Designing a successful trading agent for supply chain management , 2006, AAMAS '06.

[25]  Jason Miller,et al.  Controlling a supply chain agent using value-based decomposition , 2006, EC '06.

[26]  Peter Stone,et al.  Adapting in agent-based markets: a study from TAC SCM , 2007, AAMAS '07.

[27]  Michael P. Wellman,et al.  Empirical game-theoretic analysis of the TAC Supply Chain game , 2007, AAMAS '07.

[28]  Peter Stone,et al.  Adapting Price Predictions in TAC SCM , 2007, AMEC/TADA.

[29]  Maria L. Gini,et al.  A predictive empirical model for pricing and resource allocation decisions , 2007, ICEC.

[30]  Norman M. Sadeh,et al.  Using Information Gain to Analyze and Fine Tune the Performance of Supply Chain Trading Agents , 2007, AMEC/TADA.

[31]  Maria L. Gini,et al.  Efficient Statistical Methods for Evaluating Trading Agent Performance , 2007, AAAI.

[32]  Patrick R. Jordan,et al.  Best-first search for approximate equilibria in empirical games , 2007, AAAI 2007.

[33]  Dwight Branvold,et al.  Procurement Risk Management (PRM) at Hewlett-Packard Company , 2008, Interfaces.

[34]  Michael P. Wellman,et al.  Searching for approximate equilibria in empirical games , 2008, AAMAS.

[35]  Maria L. Gini,et al.  Coordinating Decisions in a Supply-Chain Trading Agent , 2008, AMEC/TADA.

[36]  John Collins,et al.  An experiment management framework for TAC SCM agent evaluation , 2009 .

[37]  Maria Gini,et al.  Flexible Decision Support in a DynamicBusiness Network , 2009 .

[38]  Norman M. Sadeh,et al.  The 2007 procurement challenge: A competition to evaluate mixed procurement strategies , 2009, Electron. Commer. Res. Appl..

[39]  Maria L. Gini,et al.  Flexible Decision Control in an Autonomous Trading Agent , 2007, Electron. Commer. Res. Appl..

[40]  Maria L. Gini,et al.  Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges , 2007, Decis. Support Syst..

[41]  Michael P. Wellman,et al.  Forecasting market prices in a supply chain game , 2007, AAMAS '07.

[42]  Maria L. Gini,et al.  Toward Human-Agent Competition in TAC SCM , 2009 .

[43]  D. Simchi-Levi,et al.  A Portfolio Approach to Procurement Contracts , 2005 .