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David Pardoe and Peter Stone. Bidding for Customer Orders in TAC SCM. In AAMAS 2004 Workshop on Agent Mediated Electronic Commerce VI: Theories for and Engineering of Distributed Mechanisms and Systems, July 2004.
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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 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.
@InProceedings{AMEC04-proc-tac,
author="David Pardoe and Peter Stone",
title="Bidding for Customer Orders in {TAC} {SCM}",
booktitle="{AAMAS} 2004 Workshop on Agent Mediated Electronic Commerce {VI}: Theories for and Engineering of Distributed Mechanisms and Systems",
month="July",year="2004",
abstract={
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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