The 2001 Trading Agent Competition was the second in a series of events aiming to shed light on research issues in automating trading strategies. Based on a challenging market scenario in the domain of travel shopping, the competition presents agents with difficult issues in bidding strategy, market prediction, and resource allocation. Entrants in 2001 demonstrated substantial progress over the prior year, with the overall level of competence exhibited suggesting that trading in online markets is a viable domain for highly autonomous agents.

... this paper reports on the development of a heuristic decision making framework that an autonomous agent can exploit to tackle the problem of bidding across multiple auctions with varying start and end times and with varying protocols (including English, Dutch and Vickrey). The framework is flexible, configurable, and enables the agent to adopt varying tactics and strategies that attempt to ensure that the desired item is delivered in a manner consistent with the user's preferences. Given this large space of possibilities, we employ a genetic algorithm to search (offline) for effective strategies in common classes of environment. The strategies that emerge from this evolution are then codified into the agent's reasoning behaviour so that it can select the most appropriate strategy to employ in its prevailing circumstances. The proposed framework has been implemented in a simulated marketplace environment and its effectiveness has been empirically demonstrated.

This article summarizes the bidding algorithms developed for the on-line Trading Agent Competition held in July, 2000 in Boston. At its heart, the article describes 12 of the 22 agent strategies in terms of (i) bidding strategy, (ii) allocation strategy, (iii) special approaches, and (iv) team motivations. The common and distinctive features of these agent strategies are highlighted. In addition, experimental results are presented that give some insights as to why the top-scoring agents' strategies were most eective

In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper, we present a machine-learning approach to this problem. The technique is based on a new and general boosting-based algorithm for conditional density estimation problems of this kind. This algorithm, which we present in detail, is at the heart of ATTac-2001, a top-scoring agent in the recent Trading Agent Competition (TAC-01). We describe how ATTac-2001 works, the results of the competition, and controlled experiments evaluating the effectiveness of price prediction in auctions.

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this article reports upon the design, implementation and evaluation of our particular trading agent (called SouthamptonTAC) in the 2002 competition

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

TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, bases its decisions on a competitive (Walrasian) analysis of the TAC travel economy. Using this Walrasian model, we construct a decision-theoretic formulation of the optimal bidding problem, which Walverine solves in each round of bidding for each good. Walverine's optimal bidding approach, as well as several other features of its overall strategy, are potentially applicable in a broad class of trading environments.

Abstract. Auctions are becoming an increasingly popular method for transacting business, especially over the Internet. This paper presents a general approach to building autonomous bidding agents to bid in multiple simultaneous auctions for interacting goods. The core of our approach is learning a model of the empirical price dynamics based on past data and using the model to analytically calculate, to the greatest extent possible, optimal bids. This approach is fully implemented as ATTac-2001, a top-scoring agent in the second Trading Agent Competition (TAC-01). ATTac-2001 uses boosting techniques to learn conditional distributions of auction clearing prices. We present experiments demonstrating the effectiveness of this predictor relative to several reasonable alternatives. 1

Abstract. We introduce FAucS, a software testbed for studying automated agent bidding strategies in simulated auctions, specifically the United States FCC wireless frequency spectrum auctions. In addition to the complexity of these auctions, which provides ample opportunities for intelligent approaches to bidding, this type of auction has huge commercial importance, each bringing in billions of dollars to governments around the world. We implement straightforward sample agents in FAucS and use them to replicate known beneficial bidding strategies in this type of auction. We then discuss potential in-depth studies of autonomous bidding agent behaviors using FAucS. The main contribution of this work is the implementation, description, and empirical validation of the FAucS testbed. We present it as a challenging and promising AI research domain. 1