Literature Survey: Opponent Models

Papers

Title	Anticipating Agent's Negotiation Strategies in an E-marketplace Using Belief Models
Author(s)	F. Teuteberg, K. Kurbel
Subject(s)
Summary
Relevance
Bibtex	​Link

Title	Benefits of Learning in Negotiation
Author(s)	D. Zeng, K. Sycara
Subject(s)	Benefits of learning, Bayesian learning, reservation values
Summary	Growing interest in e-commerce motivates research in automated negotiation. Building intelligent negotiation agents is still emerging. In contrast to most negotiation models, sequential decision model allows for learning. Learning can help understand human behaviour, but can also result in better results for the learning party. Bayesian learning of reservation values can be used to determine the zone of agreement for an issue based on the domain knowledge and bidding interactions. Concluding for one-issue, learning positively influences bargaining quality, number of exchanged proposals, and leads to a better compromise if both learn. Learning works always works better in the proposed case.
Relevance	8
Bibtex	​Link

Title	Learning an Opponent's Preferences to Make Effective Multi-Issue Negotiation Trade-Offs
Author(s)	R.M. Coehoorn, N.R. Jennings
Subject(s)	KDE Learning, Negotiation model, Concession based strategy
Summary
Relevance
Bibtex	​Link

Title	Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning
Author(s)	K. Hindriks, D. Tykhonov
Subject(s)
Summary
Relevance
Bibtex	​Link

Title	The First Automated Negotiating Agents Competition (ANAC 2010)
Author(s)	T. Baarslag, K. Hindriks, C. Jonker, S. Kraus, R. Lin
Subject(s)
Summary
Relevance
Bibtex	​Link

Meetings

Planning