Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Learning an Agent's Utility Function by Observing Behavior
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
TAC-03: a supply-chain trading competition
AI Magazine
MinneTAC Sales Strategies for Supply Chain TAC
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Botticelli: a supply chain management agent designed to optimize under uncertainty
ACM SIGecom Exchanges
PackaTAC: a conservative trading agent
ACM SIGecom Exchanges
Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
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
Designing Intelligent Software Agents for Auctions with Limited Information Feedback
Information Systems Research
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We present methods for an autonomous agent to identify dominant market conditions, such as over-supply or scarcity, and to forecast market changes. We show that market conditions can be characterized by distinguishable statistical patterns that can be learned from historic data and used, together with real-time observable information, to identify the current market regime and to forecast market changes. We use a Gaussian Mixture Model to represent the probabilities of market prices and, by clustering these probabilities, we identify different economic regimes. We show that the regimes so identified have properties that correlate with market factors that are not directly observable. We then present methods to predict regime changes. We validate our methods by presenting experimental results obtained with data from the Trading Agent Competition for Supply Chain Management.