Dynamic pricing by software agents
Computer Networks: The International Journal of Computer and Telecommunications Networking - electronic commerce
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Decision Processes in Agent-Based Automated Contracting
IEEE Internet Computing
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
MinneTAC Sales Strategies for Supply Chain TAC
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Detecting Regime Shifts: The Causes of Under- and Overreaction
Management Science
Price prediction and insurance for online auctions
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Botticelli: a supply chain management agent designed to optimize under uncertainty
ACM SIGecom Exchanges
Strategic trading agents via market modelling
ACM SIGecom Exchanges
The Penn-Lehman Automated Trading Project
IEEE Intelligent Systems
Deploying a personalized time management agent
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Smart Business Networks
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Smart business networks: how the network wins
Communications of the ACM - Smart business networks
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International Journal of Electronic Commerce
Identification and prediction of economic regimes to guide decision making in multi-agent marketplaces
Efficient statistical methods for evaluating trading agent performance
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Price prediction in a trading agent competition
Journal of Artificial Intelligence Research
Bidding for customer orders in TAC SCM
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
Flexible decision control in an autonomous trading agent
Electronic Commerce Research and Applications
Identifying and predicting economic regimes in supply chains using sales and procurement information
Proceedings of the 11th International Conference on Electronic Commerce
Proceedings of the 11th International Conference on Electronic Commerce
An agent-based market platform for Smart Grids
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Industry track
Research Commentary---Designing Smart Markets
Information Systems Research
Forecasting prices in dynamic heterogeneous product markets using multivariate prediction methods
Proceedings of the 13th International Conference on Electronic Commerce
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Agent-based competitive simulation: exploring future retail energy markets
Proceedings of the 12th International Conference on Electronic Commerce: Roadmap for the Future of Electronic Business
Real-Time Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes
Information Systems Research
Agent-assisted supply chain management: Analysis and lessons learned
Decision Support Systems
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We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict market trends. The agent can use this information for tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We present methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models. We show how this model combined with real-time observable information is used to identify the current dominant market condition and to forecast market changes over a planning horizon. Market changes are forecast via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and next day (supporting tactical decisions), while the Markov process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.