A Comparison of Bidding Strategies for Online Auctions Using Fuzzy Reasoning and Negotiation Decision Functions

Kaur, P; Goyal, M; Lu, J

A Comparison of Bidding Strategies for Online Auctions Using Fuzzy Reasoning and Negotiation Decision Functions

Kaur, P Goyal, M

Lu, J

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2017, 25 (2), pp. 425 - 438
Issue Date:: 2017-04-01

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Field	Value	Language
dc.contributor.author	Kaur, P	en_US
dc.contributor.author	Goyal, M https://orcid.org/0000-0003-2853-9393	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2017-04-01	en_US
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2017, 25 (2), pp. 425 - 438	en_US
dc.identifier.issn	1063-6706	en_US
dc.identifier.uri	http://hdl.handle.net/10453/110106
dc.description.abstract	© 1993-2012 IEEE. Bidders often feel challenged when looking for the best bidding strategies to excel in the competitive environment of multiple and simultaneous online auctions for same or similar items. Bidders face complicated issues for deciding which auction to participate in, whether to bid early or late, and how much to bid. In this paper, we present the design of bidding strategies, which aim to forecast the bid amounts for buyers at a particular moment in time based on their bidding behavior and their valuation of an auctioned item. The agent develops a comprehensive methodology for final price estimation, which designs bidding strategies to address buyers' different bidding behaviors using two approaches: Mamdani method with regression analysis and negotiation decision functions. The experimental results show that the agents who follow fuzzy reasoning with a regression approach outperform other existing agents in most settings in terms of their success rate and expected utility.	en_US
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/TFUZZ.2016.2598297	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A Comparison of Bidding Strategies for Online Auctions Using Fuzzy Reasoning and Negotiation Decision Functions	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	25	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

© 1993-2012 IEEE. Bidders often feel challenged when looking for the best bidding strategies to excel in the competitive environment of multiple and simultaneous online auctions for same or similar items. Bidders face complicated issues for deciding which auction to participate in, whether to bid early or late, and how much to bid. In this paper, we present the design of bidding strategies, which aim to forecast the bid amounts for buyers at a particular moment in time based on their bidding behavior and their valuation of an auctioned item. The agent develops a comprehensive methodology for final price estimation, which designs bidding strategies to address buyers' different bidding behaviors using two approaches: Mamdani method with regression analysis and negotiation decision functions. The experimental results show that the agents who follow fuzzy reasoning with a regression approach outperform other existing agents in most settings in terms of their success rate and expected utility.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/110106