Mertacor: a successful autonomous trading agent

• Authors: Panos Toulis;Dionisis Kehagias;Pericles A. Mitkas
• Venue: AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
• Year: 2006

In this paper we present the internal architecture and bidding mechanisms designed for Mertacor, a successful trading agent, which ended up first in the Classic Trading Agent Competition (TAC) of 2005. TAC provides a realistic benchmarking environment in which different trading agents compete with each other in order to best satisfy their clients' preferences and maximize their total profit. The "travel game" scenario of TAC involves three types of auctions; a) continuous one-sided that sell flight tickets, b) ascending multi-unit auctions for booking hotel rooms, and c) continuous double auctions for entertainment tickets. For each one of these types, we describe the techniques deployed by Mertacor. In flight auctions prices are updated according to a random walk process, thus the accurate prediction of the next update is not feasible. A key element of agent behavior in these auctions is its ability to accurately deduce the specific time in an auction, at which bidding will be profitable. In order to deal with the uncertainty due to price fluctuations in flight auctions and to provide our agent with an efficient decision mechanism, we have designed the z-heuristic framework. The goal of z-heuristic is to figure out when the price assumes its minimum value and recommend bidding at that moment. In the case of hotel auctions, Mertacor used fuzzy reasoning in conjunction with rule-based reasoning in order to predict the closing prices of hotel rooms, when historical data from past auctions are available. In order to bid efficiently in entertainment auctions, we have designed a bidding strategy whose goal is to preserve a pre-specified long-term profit. We finally present and discuss the results of agent benchmarking in the TAC Classic game.