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This paper introduces Correlated-Q (CE-Q) learning, a multiagent Q-learning algorithm based on the correlated equilibrium (CE) solution concept. CE-Q generalizes both Nash-Q and Friend-and-Foe-Q: in general-sum games, the set of correlated equilibria contains the set of Nash equilibria; in constant-sum games, the set of correlated equilibria contains the(More)
Shopbots are software agents that automatically gather and collate information from multiple on-line vendors about the price and quality of consumer goods and services. Rapidly increasing in number and sophistication, shopbots are helping more and more buyers minimize expenditure and maximize satisfaction. In response to this trend, it is anticipated that(More)
The 2001 Trading Agent Competition was the second in a series of events aiming to shed light on research issues in automating trading strategies. Based on a challenging market scenario in the domain of travel shopping, the competition presents agents with difficult issues in bidding strategy, market prediction, and resource allocation. Entrants in 2001(More)
The paper describes the design of the agent BOTTICELLI, a finalist in the 2003 Trading Agent Competition in Supply Chain Management (TAC SCM). In TAC SCM, a simulated computer manufacturing scenario, BOTTICELLI competes with other agents to win customer orders and negotiates with suppliers to procure the components necessary to complete its orders. We(More)
We e n vision a future in which the global economy and the Internet will merge and evolve together into an information economy bustling with billions of economically motivated software agents that exchange information goods and services with humans and other agents. Economic software agents will diier in important w ays from their human counterparts, and(More)
The paper describes the architecture of Brown Universityýs agent, Botticelli, a finalist in the 2003 Trading Agent Competition in Supply Chain Management (TAC SCM). In TAC SCM, a simulated computer manufacturing scenario, Botticelli competes with other agents to win customer orders and negotiates with suppliers to procure the components necessary to(More)
A general class of no-regret learning algorithms, called no-Φ-regret learning algorithms, is defined which spans the spectrum from no-external-regret learning to no-internal-regret learning and beyond. The set Φ describes the set of strategies to which the play of a given learning algorithm is compared. A learning algorithm satisfies no-Φ-regret if no(More)
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(More)