Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Algorithms for automated multilateral negotiations

Xenou Konstantia

Simple record

URIhttp://purl.tuc.gr/dl/dias/EEF4F4F7-49C6-4FF7-9190-F275E332088D-
Identifierhttps://doi.org/10.26233/heallink.tuc.69540-
Languageen-
Extent96 σελίδεςel
TitleAlgorithms for automated multilateral negotiationsen
TitleΑλγόριθμοι για αυτοματοποιημένες πολυμερείς διαπραγματεύσειςel
CreatorXenou Konstantiaen
CreatorΞενου Κωνσταντιαel
Contributor [Thesis Supervisor]Chalkiadakis Georgiosen
Contributor [Thesis Supervisor]Χαλκιαδακης Γεωργιοςel
Contributor [Committee Member]Lagoudakis Michaelen
Contributor [Committee Member]Λαγουδακης Μιχαηλel
Contributor [Committee Member]Spanoudakis Nikolaosen
Contributor [Committee Member]Σπανουδακης Νικολαοςel
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitTechnical University of Crete::School of Electrical and Computer Engineeringen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστώνel
Content SummaryIn multi-attribute negotiations, two or more intelligent automated agents sharing common or conflicting interests negotiate over various distinct issues, usually under uncertainty about the characteristics of their negotiating partners. Agents should be able to adaptively adjust their behavior during the negotiation process, by adopting efficient and effective automated negotiation techniques. This is a challenging process, since negotiators are not usually willing to reveal their private information and preferences, so as to avoid being exploited by the other participants during the negotiation. To overcome these problems and boost agent performance, a modeling of opponents’ preferences and strategy is usually incorporated—with the understanding that uncertainty regarding opponent preferences always exists in real-world settings. Opponent modeling can be usually shown to assist the agent to achieve efficient agreements, and thus to significantly increase the quality of the negotiation outcome. A multitude of negotiation strategies and opponent models have been coined and studied over the years; regardless, empirically comparing them to each other is not a straightforward exercise. To this end, the international Autonomous Negotiation Agents Competition (ANAC) was initiated in 2009, and is conducted utilizing a “standard”, purpose-built, negotiation platform (“Genius”). Genius provides a uniform, accepted by all, way of comparing state-of-the-art agent strategies. In this thesis, we systematically developed several different negotiating strategies, along with ac-companying opponent models. We employed concepts found in the literature, implemented known strategies, and proposed novel ones. This process resulted to the creation of thirteen (13) distinct agents. The developed agents were pitted against previous ANAC participants, and also against each other, with an extensive evaluation being conducted on Genius. One of our strategies was se-lected and participated in the international ANAC-2017 competition. Our thesis presents a detailed evaluation and analysis of the performance of all our agents.en
Type of ItemΔιπλωματική Εργασίαel
Type of ItemDiploma Worken
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2017-10-05-
Date of Publication2017-
SubjectMultiagent systemsen
SubjectNegotiationsen
Bibliographic CitationKonstantia Xenou, "Algorithms for automated multilateral negotiations", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2017en
Bibliographic CitationΚωνσταντία Ξένου, "Αλγόριθμοι για αυτοματοποιημένες πολυμερείς διαπραγματεύσεις", Διπλωματική Εργασία, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2017el

Available Files

Services

Statistics

 

Copyright © DIAS 2013