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We present and evaluate the design of Deep Maize, our entry in the 2005 Trading Agent Competition Supply Chain Management scenario. The central idea is to decompose the problem by estimating the value of key resources in the game. We first create a high-level production schedule that considers cross-cutting constraints and future decisions, but abstracts(More)
We introduce the TAC Ad Auctions game (TAC/AA), a new game for the Trading Agent Competition. The Ad Auctions game investigates complex strategic issues found in real sponsored search auctions that are not captured in current analytical models. We provide an overview of TAC/AA, introducing its key features and design rationale. TAC/AA will debut in summer(More)
Future market conditions can be a pivotal factor in making business decisions. We present and evaluate methods used by our agent, Deep Maize, to forecast market prices in the Trading Agent Competition Supply Chain Management Game. As a guiding principle we seek to exploit as many sources of available information as possible to inform predictions. Since(More)
The holy grail of online advertising is to target users with ads matched to their needs with such precision that the users respond to the ads, thereby increasing both advertisers' and users' value. The current approach to this challenge utilizes information about the users: their gender, their location, the websites they have visited before, and so on.(More)
The TAC SCM tournament is moving into its fourth year. In an effort to track agent progress, we present a benchmark market efficiency comparison for the tournament, in addition to prior measures of agent competency through customer bidding. Using these benchmarks we find statistically significant increases in intratournament market efficiency , whereas(More)
The TAC Supply Chain Management (TAC/SCM) game presents a challenging dynamic environment for autonomous decision-making in a salient application domain. Strategic interactions complicate the analysis of games such as TAC/SCM. since the effectiveness of a given strategy depends on the strategies played by other agents on the supply chain. The TAC tournament(More)
When exploring a game over a large strategy space, it may not be feasible or cost-effective to evaluate the payoff of every relevant strategy profile. For example, determining a profile payoff for a procedurally defined game may require Monte Carlo simulation or other costly computation. Analyzing such games poses a search problem, with the goal of(More)
The inaugural tournament for the Trading Agent Competition Ad Auctions game was held in July 2009. We describe the results, identifying key strategic behavior of the top agents in the competition. Through post-tournament simulation, we construct an empirical game using the agents and mechanism from the competition, and derive equilibria of this game. We(More)
Empirical analyses of complex games necessarily focus on a restricted set of strategies, and thus the value of empirical game models depends on effective methods for selectively exploring a space of strategies. We formulate an iterative framework for strategy exploration, and experimentally evaluate an array of generic exploration policies on three games:(More)