RoxyBot-06: An SAA^2 TAC Travel Agent

Seong Jae Lee, Amy Greenwald, Victor Naroditskiy

In this paper, we describe our entrant in the travel division of the 2006 Trading Agent Competition (TAC). At a high level, the design of many successful autonomous trading agents can be summarized as follows: (i)~price prediction: build a model of market prices; and (ii)~optimization: solve for an approximately optimal set of bids, given this model. To predict, we simulate simultaneous ascending auctions. To optimize, we apply the sample average approximation method. Both of these procedures might naturally be abbreviated SAA; hence the title of this paper. Our agent dominated the preliminary and seeding rounds of TAC Travel in 2006, and emerged as champion in the finals in a photo finish.