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A Heuristic Bidding Strategy for Buying Multiple Goods in Multiple English Auctions.

A Heuristic Bidding Strategy for Buying Multiple Goods in Multiple English Auctions.
A Heuristic Bidding Strategy for Buying Multiple Goods in Multiple English Auctions.
This paper presents the design, implementation, and evaluation of a novel bidding algorithm that a software agent can use to obtain multiple goods from multiple overlapping English auctions. Specifically, an Earliest Closest First heuristic algorithm is proposed that uses neurofuzzy techniques to predict the expected closing prices of the auctions and to adapt the agent’s bidding strategy to reflect the type of environment in which it is situated. This algorithm first identifies the set of auctions that are most likely to give the agent the best return and then, according to its attitude to risk, it bids in some other auctions that have approximately similar expected returns, but which finish earlier than those in the best return set. We show through empirical evaluation against a number of methods proposed in the multiple auction literature that our bidding strategy performs effectively and robustly in a wide range of scenarios.
Intelligent Agents, Online Auctions, Multiple English Auctions, Bidding Strategy, e-commerce
465-496
He, M
24cefbbd-e114-42cb-b4d0-094e5458a1fc
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Prugel-Bennett, A
b107a151-1751-4d8b-b8db-2c395ac4e14e
He, M
24cefbbd-e114-42cb-b4d0-094e5458a1fc
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Prugel-Bennett, A
b107a151-1751-4d8b-b8db-2c395ac4e14e

He, M, Jennings, N. R. and Prugel-Bennett, A (2006) A Heuristic Bidding Strategy for Buying Multiple Goods in Multiple English Auctions. ACM Transactions on Internet Technology, 6 (4), 465-496.

Record type: Article

Abstract

This paper presents the design, implementation, and evaluation of a novel bidding algorithm that a software agent can use to obtain multiple goods from multiple overlapping English auctions. Specifically, an Earliest Closest First heuristic algorithm is proposed that uses neurofuzzy techniques to predict the expected closing prices of the auctions and to adapt the agent’s bidding strategy to reflect the type of environment in which it is situated. This algorithm first identifies the set of auctions that are most likely to give the agent the best return and then, according to its attitude to risk, it bids in some other auctions that have approximately similar expected returns, but which finish earlier than those in the best return set. We show through empirical evaluation against a number of methods proposed in the multiple auction literature that our bidding strategy performs effectively and robustly in a wide range of scenarios.

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More information

Published date: 2006
Keywords: Intelligent Agents, Online Auctions, Multiple English Auctions, Bidding Strategy, e-commerce
Organisations: Agents, Interactions & Complexity, Southampton Wireless Group

Identifiers

Local EPrints ID: 264473
URI: http://eprints.soton.ac.uk/id/eprint/264473
PURE UUID: af057971-9754-4295-a1b7-2f302d7da778

Catalogue record

Date deposited: 06 Sep 2007
Last modified: 02 Dec 2019 21:04

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Contributors

Author: M He
Author: N. R. Jennings
Author: A Prugel-Bennett

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