{"type":"ScholarlyArticle","title":"POPPONENT: Highly accurate, individually and socially efficient opponent preference model in bilateral multi issue negotiations (Extended Abstract)","authors":[{"name":"Farhad Zafari","slug":"farhad-zafari","given":"Farhad","family":"Zafari","affiliation":"University of Isfahan , Isfahan , Iran"},{"name":"Faria Nassiri-Mofakham","slug":"faria-nassiri-mofakham","given":"Faria","family":"Nassiri-Mofakham","affiliation":"* Swinburne University of Technology , Melbourne , Australia"}],"abstract":"In automated bilateral multi issue negotiations, two intelligent automated agents negotiate on behalf of their owners over many issues in order to reach an agreement. Modeling the opponent can excessively boost the performance of the agents and increase the quality of the negotiation outcome. State of the art models accomplish this by considering some assumptions about the opponent which restricts their applicability in real scenarios. In this paper, a less restricted technique where perceptron units (POPPONENT) are applied in modelling the preferences of the opponent is proposed. This model adopts a Multi Bipartite version of the Standard Gradient Descent search algorithm (MBGD) to find the best hypothesis, which is the best preference profile. In order to evaluate the accuracy and performance of this proposed opponent model, it is compared with the state of the art models available in the Genius repository. This results in the devised setting which approves the higher accuracy of POPPONENT compared to the most accurate state of the art model. Evaluating the model in the real world negotiation scenarios in the Genius framework also confirms its high accuracy in relation to the state of the art models in estimating the utility of offers. The findings here indicate that the proposed model is individually and socially efficient. This proposed MBGD method could also be adopted in similar practical areas of Artificial Intelligence.","doi":"10.24963/ijcai.2017/730","url":"https://ijcai.org/proceedings/2017/730","pdf":"https://ijcai.org/proceedings/2017/0730.pdf","markdown":"https://ijcai.org/proceedings/2017/730.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/730","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Journal track","track":"Journal track","number":730,"pages":{"first":5100,"last":5104},"keywords":["Knowledge Representation, Reasoning, and Logic: Preferences","Agent-based and Multi-agent Systems: Agreement Technologies: Negotiation and Contract-Based Systems","Knowledge Representation, Reasoning, and Logic: Preference modelling and preference-based reasoning","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Baarslag et al., 2012] Tim Baarslag, Koen Hindriks, Catholijn Jonker, Sarit Kraus, and Raz Lin. 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