Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
TAC-03: a supply-chain trading competition
AI Magazine
TacTex-03: a supply chain management agent
ACM SIGecom Exchanges
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
CMieux: adaptive strategies for competitive supply chain trading
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Supply chains are a current, challenging problem for agent-based electronic commerce. Motivated by the Trading Agent Competition Supply Chain Management (TAC SCM) scenario, we consider an individual supply chain agent as having three major subtasks: acquiring supplies, selling products, and managing its local manufacturing process. In this paper, we focus on the sales subtask. In particular, we consider the problem of finding the set of bids to customers in simultaneous reverse auctions that maximizes the agentýs expected profit. The key technical challenges we address are i) predicting the probability that a customer will accept a particular bid price, and ii) searching for the most profitable set of bids. We first compare several machine learning approaches to estimating the probability of bid acceptance. We then present a heuristic approach to searching for the optimal set of bids. Finally, we perform experiments in which we apply our learning method and bidding method during actual gameplay to measure the impact on agent performance. Full details can be found in the extended version of this paper [1].