Building automated negotiation strategies enhanced by MLP and GR neural networks for opponent agent behaviour prognosis

Roussaki, I; Papaioannou, I; Anangostou, M

dc.contributor.author	Roussaki, I	en
dc.contributor.author	Papaioannou, I	en
dc.contributor.author	Anangostou, M	en
dc.date.accessioned	2014-03-01T02:44:29Z
dc.date.available	2014-03-01T02:44:29Z
dc.date.issued	2007	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31848
dc.subject	MLP & GR neural networks	en
dc.subject	Negotiating agents	en
dc.subject	NN-assisted negotiation strategies	en
dc.subject	Opponent behaviour prediction	en
dc.subject.other	Decision making	en
dc.subject.other	Intelligent agents	en
dc.subject.other	Learning systems	en
dc.subject.other	Corporate owners	en
dc.subject.other	Learning techniques	en
dc.subject.other	Negotiating agents	en
dc.subject.other	Opponent behaviour prediction	en
dc.subject.other	Neural networks	en
dc.title	Building automated negotiation strategies enhanced by MLP and GR neural networks for opponent agent behaviour prognosis	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-73007-1_19	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-73007-1_19	en
heal.publicationDate	2007	en
heal.abstract	A quite challenging research field in the artificial intelligence domain is the design and evaluation of agents handling automated negotiations on behalf of their human or corporate owners. This paper aims to enhance such agents with techniques enabling them to predict their opponents' negotiation behaviour and thus achieve more profitable results and better resource utilization. The proposed learning techniques are based on MLP and GR neural networks (NNs) that are used mainly to detect at an early stage the cases where agreements are not achievable, supporting the decision of the agents to withdraw or not from the specific negotiation thread. The designed NN-assisted negotiation strategies have been evaluated via extensive experiments and are proven to be very useful. © Springer-Verlag Berlin Heidelberg 2007.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-540-73007-1_19	en
dc.identifier.volume	4507 LNCS	en
dc.identifier.spage	152	en
dc.identifier.epage	161	en