Planning and acting in partially observable stochastic domains
Artificial Intelligence
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SIAM Journal on Optimization
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SIAM Journal on Optimization
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IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Introduction to Stochastic Programming
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UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Bidding under uncertainty: theory and experiments
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Negotiation mechanism for TAC SCM component market
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
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ACM SIGecom Exchanges
TacTex-03: a supply chain management agent
ACM SIGecom Exchanges
Botticelli: a supply chain management agent designed to optimize under uncertainty
ACM SIGecom Exchanges
Controlling a supply chain agent using value-based decomposition
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Empirical mechanism design: methods, with application to a supply-chain scenario
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Strategies in supply chain management for the Trading Agent Competition
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Forecasting market prices in a supply chain game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Equilibrium Analysis Based Pricing Mechanism in a MultiAgent Based Supply-Chain System
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Forecasting market prices in a supply chain game
Electronic Commerce Research and Applications
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
An analysis of the 2004 supply chain management trading agent competition
AMEC'05 Proceedings of the 2005 international conference on Agent-Mediated Electronic Commerce: designing Trading Agents and Mechanisms
AMEC'05 Proceedings of the 2005 international conference on Agent-Mediated Electronic Commerce: designing Trading Agents and Mechanisms
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In this paper, we combine two approaches to handling uncertainty: we use techniques for finding optimal solutions in the expected sense to solve combinatorial optimization problems in an online setting. The problem we address is the scheduling component of the Trading Agent Competition in Supply Chain Management (TAC SCM) problem, a combinatorial optimization problem with inherent uncertainty (see www.sics.se/tac/). This problem is formulated as a stochastic program, and is solved using the sample average approximation (SAA) method in an online setting to find today's optimal schedule, given probabilistic models of the future. This optimization procedure forms the heart of Botticelli, one of the finalists in the TAC SCM 2003 competition. Two sets of experiments are described, using one and two days' worth of information about the future. In the two day experiments (using one day's worth of information about the future), it is shown that SAA outperforms the expected value method, which solves a deterministic variant of the problem assuming all stochastic inputs have deterministic values equal to their expected values. In the three day experiments (using two days' worth of information about the future), it is shown that SAA with look ahead outperforms greedy SAA. This approach generalizes to N days of lookahead, and since the problem setting is one of online optimization, the benefits of two day lookahead accrue rapidly.