Walverine: a Walrasian trading agent

Cheng, Shih-Fen; Leung, Evan; Lochner, Kevin M.; O'Malley, Kevin; Reeves, Daniel M.; Schvartzman, Julian L.; Wellman, Michael P.

doi:10.1016/j.dss.2003.10.005

Cited by 27 publications

(26 citation statements)

References 9 publications

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“…For example, in game 6341, there is very little competition and the closing prices for T (S) are 7(12), 92 (27), 70 (53), and 62 (7). In this situation, the best strategy is to buy all flights earlier; since the agents can always get the hotels they want.…”

Section: A Classifying Tac Environmentsmentioning

confidence: 99%

“…20 The firing level of the rules is computed by the operator. That is, (7) where are the membership functions of the corresponding fuzzy sets and , respectively. According to the Sugeno controller's definition, the crisp control action (i.e., the predicted increase of hotel closing price) of the rule base is obtained by (8) 19 The use of other fuzzy logic controllers, such as the conventional Mamdani controller [56] is also possible, and such of controllers could improve the performance of our algorithms still further.…”

Section: ) Fuzzy Reasoning About Hotel Closing Pricesmentioning

confidence: 99%

“…Flight 6 These packages are assembled by the agent bidding in a number of auctions in which the other bidders are other competition entrants. 7 Customers are not allowed to change their hotels during the stay. 8 For full details, see http://www.sics.se/tac.…”

Section: Trading Agent Competitionmentioning

confidence: 99%

“…In each TAC trading game, there are eight software agents (entrants to the competition) that compete against each other in a variety of auctions to assemble travel packages for their individual customers according to their preferences for the trip. 6 A valid travel package for an individual customer consists of: i) a round trip flight during a five-day period (between TACtown and Tampa), and ii) a stay at the same hotel 7 for every night between their arrival and departure dates. Moreover, arranging appropriate entertainment events during the trip increases the utility for the customers.…”

Section: Trading Agent Competitionmentioning

confidence: 99%

“…UMBCTAC balances the minimal risk and the maximum return to find the best travel plan for its customers. Walverine [7] predicts the hotel closing prices by calculating the Walrasian competitive equilibrium of the game. whitebear [47] uses a randomised greedy algorithm to calculate the price of each commodity bought or sold and uses Bayesian analysis to compute the minimum and average value of the flight's determinant factor.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: A Classifying Tac Environmentsmentioning

confidence: 99%