A trading agent competition for the research community

MP Wellman and PR Wurman IJCAI-99 Workshop on Agent-Mediated Electronic Commerce, 1999. Abstract We discuss the design of a trading agent competition to be held in conjunction with ICMAS-00. This design will be revised based on deliberations…

Trading Agents

MP Wellman Morgan & Claypool Publishers, Synthesis Lectures on Artificial Intelligence and Machine Learning Abstract Automated trading in electronic markets is one of the most common and consequential applications of autonomous software…

Strategy and Mechanism Lessons from the First Ad Auctions Trading Agent Competition

PR Jordan, MP Wellman, and G Balakrishnan Proceedings of the 11th ACM Conference on Electronic Commerce, pages 287–296, July 2010. Abstract The inaugural tournament for the Trading Agent Competition Ad Auctions game was held in July 2009.…

Distributed feedback control for decision making on supply chains

Decision makers on supply chains face an uncertain, dynamic, and strategic multiagent environment. We report on Deep Maize, an agent we designed to participate in the 2003 Trading Agent Competition, Supply Chain Management (TAC/SCM) game. Our design employs an idealized equilibrium analysis of the SCM game to factor out the strategic aspects of the environment, and to define an expected profitable zone of operation. Deep Maize applies distributed feedback control to coordinate its separate functional modules and maintain its environment in the desired zone, despite the uncertainty and dynamism. We evaluate our design with results from the TAC/SCM tournament as well as from controlled experiments conducted after the competition.

Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition

E-commerce increasingly provides opportunities for autonomous bidding agents: computer programs that bid in electronic markets without direct human intervention. Automated bidding strategies for an auction of a single good with a known valuation are fairly straightforward; designing strategies for simultaneous auctions with interdependent valuations is a more complex undertaking. This book presents algorithmic advances and strategy ideas within an integrated bidding agent architecture that have emerged from recent work in this fast-growing area of research in academia and industry. The authors analyze several novel bidding approaches that developed from the Trading Agent Competition (TAC), held annually since 2000. The benchmark challenge for competing agents—to buy and sell multiple goods with interdependent valuations in simultaneous auctions of different types—encourages competitors to apply innovative techniques to a common task. The book traces the evolution of TAC and follows selected agents from conception through several competitions, presenting and analyzing detailed algorithms developed for autonomous bidding. Autonomous Bidding Agents provides the first integrated treatment of methods in this rapidly developing domain of AI. The authors—who introduced TAC and created some of its most successful agents—offer both an overview of current research and new results.

Learning Improved Entertainment Trading Strategies for the TAC Travel Game

For almost five years we have continually operated a simulation testbed exploring a variety of strategies for the TAC Travel game. Building on techniques developed in our recent study of continuous double auctions, we performed an equilibrium analysis of our testbed data, and employed reinforcement learning in the equilibrium environment to derive a new entertainment strategy for this domain. A second iteration of this process led to further improvements. We thus demonstrate that interleaving empirical game-theoretic analysis with reinforcement learning in an effective method for generating stronger trading strategies in this domain.

Forecasting Market Prices in a Supply Chain Game

Predicting the uncertain and dynamic future of market conditions on the supply chain, as reflected in prices, is an essential component of effective operational decision-making. We present and evaluate methods used by our agent, Deep Maize, to forecast market prices in the trading agent competition supply chain management game (TAC/SCM). We employ a variety of machine learning and representational techniques to exploit as many types of information as possible, integrating well-known methods in novel ways. We evaluate these techniques through controlled experiments as well as performance in both the main TAC/SCM tournament and supplementary Prediction Challenge. Our prediction methods demonstrate strong performance in controlled experiments and achieved the best overall score in the Prediction Challenge.

Designing the Ad Auctions Game for the Trading Agent Competition

We introduce the TAC Ad Auctions game (TAC/AA), a new game for the Trading Agent Competition. The Ad Auctions game investigates complex strategic issues found in real sponsored search auctions that are not captured in current analytical models. We provide an overview of TAC/AA, introducing its key features and design rationale. TAC/AA will debut in summer 2009, with the final tournament commencing in conjunction with the TADA-09 workshop.