Comparing the performance of MLP and RBF neural networks employed by negotiating intelligent agents

Papaioannou, IV; Roussaki, IG; Anagnostou, ME

dc.contributor.author	Papaioannou, IV	en
dc.contributor.author	Roussaki, IG	en
dc.contributor.author	Anagnostou, ME	en
dc.date.accessioned	2014-03-01T02:51:03Z
dc.date.available	2014-03-01T02:51:03Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35328
dc.subject.other	Electronic commerce	en
dc.subject.other	Engineering research	en
dc.subject.other	Intelligent agents	en
dc.subject.other	Learning systems	en
dc.subject.other	Optimization	en
dc.subject.other	Profitability	en
dc.subject.other	Automated negotiations	en
dc.subject.other	Negotiation strategies	en
dc.subject.other	Neural networks	en
dc.title	Comparing the performance of MLP and RBF neural networks employed by negotiating intelligent agents	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IAT.2006.49	en
heal.identifier.secondary	4052983	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IAT.2006.49	en
heal.publicationDate	2007	en
heal.abstract	One of the means that improve the performance and sophistication of systems in the e-business domain is mobile intelligent agents' technology. In this framework, a quite challenging research field is the design and evaluation of agents handling automated negotiations on behalf of their human or corporate owners. This paper proposes to enhance such agents with learning techniques, in order to achieve more profitable results for the parties they represent. The proposed learning techniques are based on MLP or RBF neural networks (NNs) and are quite lightweight. They aim to reduce the cases of unsuccessful negotiations and maximize the client's utility. The designed NN-assisted negotiation strategies have been compared and empirically evaluated via numerous experiments. © 2006 IEEE.	en
heal.journalName	Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06	en
dc.identifier.doi	10.1109/IAT.2006.49	en
dc.identifier.spage	602	en
dc.identifier.epage	609	en