This article summarizes 16 agent strategies that were designed for the 2002 Trading Agent Competition. Agent architects use numerous general-purpose AI techniques, including machine learning, planning, partially observable Markov decision processes, Monte Carlo simulations, and multiagent systems. Ultimately, the most successful agents were primarily heuristic based and domain specific.

In TAC-00, agent designs were primarily centered around designing algorithms a tripod are sometimes bundled with the camera to solve an NPcomplete optimization and sometimes auctioned separately. However, by the second year, it for the next generation of trading agents, became common knowledge that this problem autonomous bidding in simultaneous auctions was tractable for the TAC travel game parameters. During the second year, agent designs focused Simultaneous auctions, which characterize on estimating clearing prices, and some internet sites such as eBay.com, Agent design in and substitutable goods are on offer. Complementary TAC-02, however, cannot be described so succinctly.