Agent-oriented software engineering for successful TAC participation

Fritschi, Clemens; Dorer, Klaus

doi:10.1145/544741.544753

Cited by 19 publications

(11 citation statements)

References 6 publications

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“…A trading agent using the early bird heuristic decides on a resource allocation at the very beginning of game and does not change it. This heuristic was identified as contributing to LivingAgents' [Fritschi and Dorer, 2002] success as the top scorer in TAC'01. The heuristic relies on perfect prediction assumption, which means that a trading agent can correctly predict the "exact" clearing price for each auction at the beginning of a game.…”

Section: Early Bird and Cautious Biddermentioning

confidence: 99%

Strategies and Heuristics Used by the UMBCTAC Agent in the Third Trading Agent Competition

Li¹,

Finin²,

Shi³

et al. 2005

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The UMBCTAC agent was one of the top ranked agents in the third international Trading Agent Competition (TAC'02). This paper describes and evaluates the key heuristics used by UMBCTAC, including the early bird heuristic, the balance heuristic, and the separation heuristic. We developed a simple gainrisk model to search safe and profitable allocations for hotel rooms and airline tickets. We also used a novel probabilistic approach to dynamically allocate entertainment tickets and bid in entertainment auctions. We conclude with a description of ongoing and planned work.

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Section: Early Bird and Cautious Biddermentioning

confidence: 99%

Strategies and Heuristics Used by the UMBCTAC Agent in the Third Trading Agent Competition

Li¹,

Finin²,

Shi³

et al. 2005

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“…14 In more detail, each customer can choose from inflight day (1-4) and outflight day (2-5) and hotel type (T or S). This means that in total there are 20 valid packages for each customer (see Appendix A for more details).…”

Section: Allocatormentioning

confidence: 99%

Designing a Successful Trading Agent: A Fuzzy Set Approach

Jennings

2004

IEEE Trans. Fuzzy Syst.

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Abstract-Software agents are increasingly being used to represent humans in online auctions. Such agents have the advantages of being able to systematically monitor a wide variety of auctions and then make rapid decisions about what bids to place in what auctions. They can do this continuously and repetitively without losing concentration. To provide a means of evaluating and comparing (benchmarking) research methods in this area the trading agent competition (TAC) was established. This competition involves a number of agents bidding against one another in a number of related auctions (operating different protocols) to purchase travel packages for customers. Against this background, this paper describes the design, implementation and evaluation of SouthamptonTAC, one of the most successful participants in both the Second and the Third International Competitions. Our agent uses fuzzy techniques at the heart of its decision making: to make bidding decisions in the face of uncertainty, to make predictions about the likely outcomes of auctions, and to alter the agent's bidding strategy in response to the prevailing market conditions.

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“…It uses a greedy, heuristic search to find the travel packages for customers. livingagents [Fritschi and Dorer 2002] bases its decisions on closing price data for the various hotels in past games and it buys all the flights needed at the beginning of the game. It also buys/sells entertainment tickets at a fixed price of 80.…”

Section: Related Workmentioning

confidence: 99%

SouthamptonTAC

Jennings

2003

ACM Trans. Internet Technol.

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Software agents are increasingly being used to represent humans in on-line auctions. Such agents have the advantages of being able to systematically monitor a wide variety of auctions and then make rapid decisions about what bids to place in what auctions. They can do this continuously and repetitively without losing concentration. Moreover, in complex multiple auction settings, agents may need to modify their behavior in one auction depending on what is happening in another. To provide a means of evaluating and comparing (benchmarking) research methods in this area, the Trading Agent Competition (TAC) was established. This competition involves a number of agents bidding against one another in a number of related auctions (operating different protocols) to purchase travel packages for customers. Against this background, this artcle describes the design, implementation and evaluation of our adaptive autonomous trading agent, SouthamptonTAC, one of the most successful participants in TAC 2002.

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Agent-oriented software engineering for successful TAC participation

Cited by 19 publications

References 6 publications

Strategies and Heuristics Used by the UMBCTAC Agent in the Third Trading Agent Competition

Strategies and Heuristics Used by the UMBCTAC Agent in the Third Trading Agent Competition

Designing a Successful Trading Agent: A Fuzzy Set Approach

SouthamptonTAC

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