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 agent's 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.

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In P. Faratin and J.A. Rodriguez-Aguilar, editors, Agent Mediated Electronic Commerce VI: Theories for and Engineering of Distributed Mechanisms and Systems (AMEC 2004), Lecture Notes in Artificial Intelligence, 3435, 143-157, Berlin, 2005. Springer Verlag.

@InCollection{AMEC04-tac, title={Bidding for Customer Orders in TAC SCM}, author={David Pardoe and Peter Stone}, booktitle={Agent Mediated Electronic Commerce VI: Theories for and Engineering of Distributed Mechanisms and Systems (AMEC 2004)}, volume={3435}, editor={P. Faratin and J.A. Rodriguez-Aguilar}, series={Lecture Notes in Artificial Intelligence}, address={Berlin}, publisher={Springer Verlag}, pages={143-157}, url="http://nn.cs.utexas.edu/?AMEC04-tac", year={2005} }