Decision Making in Multi-Issue e-Market Auction Using Fuzzy Attitudes

Goyal, ML; Lu, J; Zhang, G

Decision Making in Multi-Issue e-Market Auction Using Fuzzy Attitudes

Goyal, ML Lu, J

Zhang, G

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Publisher:: University of Talca
Publication Type:: Journal Article
Citation:: Journal of Theoretical and Applied Electronic Commerce Research, 2008, 3 (2), pp. 97 - 110
Issue Date:: 2008-01

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Field	Value	Language
dc.contributor.author	Goyal, ML	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Journal of Theoretical and Applied Electronic Commerce Research, 2008, 3 (2), pp. 97 - 110	en_US
dc.identifier.issn	0718-1876	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8929
dc.description.abstract	Online auctions are one of the most effective ways of negotiation of salable goods over the internet. Software agents are increasingly being used to represent humans in online auctions. These agents can systematically monitor a wide variety of auctions and can make rapid decisions about what bids to place in what auctions. To be successful in open multi-agent environments, agents must be capable of adapting different strategies and tactics to their prevailing circumstances. This paper presents a software test-bed for studying autonomous bidding strategies in simulated auctions for procuring goods. It shows that agents bidding strategy explore the attitudes and behaviors that help agents to manage dynamic assessment of prices of goods given the different criteria and scenario conditions. Our agent also uses fuzzy techniques for the decision making: to make decisions about the outcome of auctions, and to alter the agents bidding strategy in response to the different criteria and market conditions.	en_US
dc.publisher	University of Talca	en_US
dc.relation.ispartof	Journal of Theoretical and Applied Electronic Commerce Research	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Decision Making in Multi-Issue e-Market Auction Using Fuzzy Attitudes	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	3	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0899 Other Information and Computing Sciences	en_US
utslib.for	1505 Marketing	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.consider-herdc	true	en_US
pubs.issue	2	en_US
pubs.volume	3	en_US

Abstract:

Online auctions are one of the most effective ways of negotiation of salable goods over the internet. Software agents are increasingly being used to represent humans in online auctions. These agents can systematically monitor a wide variety of auctions and can make rapid decisions about what bids to place in what auctions. To be successful in open multi-agent environments, agents must be capable of adapting different strategies and tactics to their prevailing circumstances. This paper presents a software test-bed for studying autonomous bidding strategies in simulated auctions for procuring goods. It shows that agents bidding strategy explore the attitudes and behaviors that help agents to manage dynamic assessment of prices of goods given the different criteria and scenario conditions. Our agent also uses fuzzy techniques for the decision making: to make decisions about the outcome of auctions, and to alter the agents bidding strategy in response to the different criteria and market conditions.

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

http://hdl.handle.net/10453/8929