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UBC Theses and Dissertations

Computational problems in multiagent systems Jiang, Albert Xin


There has been recent interest from the computer science community on multiagent systems, where multiple autonomous agents, each with their own utility functions, act according to their own interests. In this thesis, we apply techniques developed in other areas of CS to solve two computational problems in multiagent systems: Action Graph Games: Action Graph Games (AGGs), first proposed in [Bhat and Leyton-Brown 2004], are a fully expressive game representation which can compactly express strict and context-specific independence and anonymity structure in players' utility functions. We present an efficient algorithm for computing expected payoffs under mixed strategy profiles. This algorithm runs in time polynomial in the size of the AGG representation (which is itself polynomial in the number of players when the in-degree of the action graph is bounded). We also present an extension to the AGG representation which allows us to compactly represent a wider variety of structured utility functions. Learning and planning i n online auction environments: There is much active research into the design of automated bidding agents, particularly for environments that involve multiple auctions. These settings are complex partly because an agent's optimal strategy depends on information about other bidder's preferences. When bidders' valuation distributions are not known ex ante, machine learning techniques can be used to approximate them from historical data. It is a characteristic feature of auctions, however, that information about some bidders' valuations is systematically concealed. This occurs in the sense that some bidders may fail to bid at all because the asking price exceeds their valuations, and also in the sense that a high bidder may not be compelled to reveal her valuation. Ignoring these "hidden bids" can introduce bias into the estimation of valuation distributions. To overcome this problem, we proposed an EM-base algorithm. We validate the algorithm experimentally using agents that react to their environments both decision-theoretically and game-theoretically, using both synthetic and real-world (eBay) datasets. We show that our approach estimates bidders' valuation distributions and the distribution over the true number of bidders significantly more accurately than more straightforward density estimation techniques. Bidding agents using the estimated distributions from our EM approach were able to outperform bidding agents using the straightforward estimates, in both decision-theoretic and game-theoretic settings.

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