Employing neural networks to assist negotiating intelligent agents

Roussaki, I; Papaioannou, I; Anagnostou, M

dc.contributor.author	Roussaki, I	en
dc.contributor.author	Papaioannou, I	en
dc.contributor.author	Anagnostou, M	en
dc.date.accessioned	2014-03-01T02:50:21Z
dc.date.available	2014-03-01T02:50:21Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35083
dc.subject.other	Communication systems	en
dc.subject.other	Computational methods	en
dc.subject.other	Mobile agents	en
dc.subject.other	Neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Web services	en
dc.subject.other	Human intelligence	en
dc.subject.other	Mobile intelligent agents	en
dc.subject.other	Network nodes	en
dc.subject.other	Intelligent agents	en
dc.title	Employing neural networks to assist negotiating intelligent agents	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1049/cp:20060631	en
heal.identifier.secondary	http://dx.doi.org/10.1049/cp:20060631	en
heal.publicationDate	2006	en
heal.abstract	Artificial intelligence is one of the various disciplines that need to be employed towards the vision of ambient intelligence. Mobile intelligent agents introduce a powerful technology that may assist the market penetration of services and products offered online in intelligent environments. Such agents have the potential to improve the efficiency, proactive behaviour and performance of computing and communication systems in such domains. In this framework, the design and evaluation of agents responsible for handling automated negotiations on behalf of their human or corporate owners is a challenging research field. In this paper these agents are enhanced with learning techniques, in order to better simulate the human intelligence and increase the profits of their owners. As mobile agents have reduced processing capabilities and may need to migrate to foreign network nodes, the learning mechanisms they employ should require minimal resources and be computationally efficient. The proposed learning technique is based on a specially designed neural network (NN), is quite lightweight, and is appropriate for agents that represent clients in automated negotiations in intelligent environments. Exploited by agents that use a fair relative tit-for-tat negotiation strategy, it aims to increase the ratio of successful negotiations and maximize the utility of the client. The designed NN-assisted negotiation strategy has been empirically evaluated via numerous experiments under various conditions.	en
heal.journalName	IET Conference Publications	en
dc.identifier.doi	10.1049/cp:20060631	en
dc.identifier.issue	518	en
dc.identifier.spage	101	en
dc.identifier.epage	110	en