Towards successful automated negotiations based on Neural Networks

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	2014-03-01T02:44:21Z
dc.date.available	2014-03-01T02:44:21Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31763
dc.subject	Automated Negotiation	en
dc.subject	E Commerce	en
dc.subject	Empirical Evaluation	en
dc.subject	Intelligent Agent	en
dc.subject	Mobile Agent	en
dc.subject	Numerical Experiment	en
dc.subject	Process Capability	en
dc.subject	Profitability	en
dc.subject	Neural Network	en
dc.subject.other	Electronic commerce	en
dc.subject.other	Information services	en
dc.subject.other	Intelligent agents	en
dc.subject.other	Learning systems	en
dc.subject.other	Mobile agents	en
dc.subject.other	E-business domain	en
dc.subject.other	E-commerce channels	en
dc.subject.other	Remote communication	en
dc.subject.other	Neural networks	en
dc.title	Towards successful automated negotiations based on Neural Networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICIS-COMSAR.2006.84	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICIS-COMSAR.2006.84	en
heal.identifier.secondary	1652034	en
heal.publicationDate	2006	en
heal.abstract	Mobile intelligent agents may assist the rapid and wide market penetration of services and products offered via e-commerce channels, as they improve the performance and sophistication of systems in the e-business domain. In this framework, the design and evaluation of agents handling automated negotiations on behalf of their human or corporate owners is a challenging research field. This paper proposes to enhance such agents with learning techniques, in order to achieve more profitable results for the parties they represent. As mobile agents have reduced processing capabilities and have to remotely communicate with their peers or migrate to foreign network nodes, the learning mechanisms they employ should require minimal resources. The proposed learning technique is based on neural networks (NNs) and is quite lightweight. It aims to reduce the cases of unsuccessful negotiations and maximize the client's utility. The designed NN-assisted negotiation strategy1 has been empirically evaluated via numerous experiments. © 2006 IEEE.	en
heal.journalName	Proceedings - 5th IEEE/ACIS Int. Conf. on Comput. and Info. Sci., ICIS 2006. In conjunction with 1st IEEE/ACIS, Int. Workshop Component-Based Software Eng., Softw. Archi. and Reuse, COMSAR 2006	en
dc.identifier.doi	10.1109/ICIS-COMSAR.2006.84	en
dc.identifier.volume	2006	en
dc.identifier.spage	464	en
dc.identifier.epage	471	en