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Browsing by Author "Dr. Peter Wurman, Chair"

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    The Application of Monte Carlo Sampling to Sequential Auction Games with Incomplete Information: An Empirical Study
    (2001-10-12) Zhu, Weili; Dr. Peter Wurman, Chair; Dr. Michael Young, Member; Dr. Robert St. Amant, Member
    In this thesis, I develop a sequential auction model and design a bidding agent for it. This agent uses Monte Carlo sampling to 'learn' from a series sampled games. I use a game theory research toolset called GAMBIT to implement the model and collect some experimental data. The data shows the effect of different factors that impact on our agent's performance, such as the sample size, the depth of game tree, etc. The data also shows that our agent performs well compared with myopic strategic agent. I also discuss the possible relaxation of different aspects in our auction model, and future research directions.
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    Effects of approximation on an iterative combinatorial auction
    (2002-01-02) Kammanahalli, Harish; Dr. Peter Wurman, Chair; Dr. Munindar Singh, Member; Dr. Laurie Williams, Member
    Combinatorial auctions are a promising way of solving the complicated heterogeneous resource allocation problem. However combinatorial auctions present inherently hard computational problems for agents and auctioneers. In particular (1) the auction must solve the winner determination problem, which is NP-complete and (2) the agents must solve the value determination problem which can be intractable when computing a precise valuation for the bundles.The winner determination problem can be addressed by (1) developing algorithms which solve the problem of computing the optimal solution; (2) developing heuristics which approximate the solution quickly. The combinatorial auctions, proposed in the literature are dependent on the optimal solution of the winner determination problem, which is intractable for large number of agents and items.In this thesis we study the effects of using approximate winner determination in a particular iterative combinatorial auction : A1BA. We categorize the effects of approximation and develop several corrective methods which reduce the errors due to approximation in several categories.

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