Literature Survey: Opponent Models

This page provides an overview of the literature survey of Mark Hendrikx. The first Section discusses the researched papers, including a summary
of the relevant details. The second Section provides an overview of all meetings. Finally, the third Section outlines a global planning.

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. Strong example of Bayesian learning
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	Effective and efficient multi-issue negotiation requires an agent to have some indication of it's opponent's preferences over the issues in the domain. Kernel Density Estimation (KDE) is used to estimate the weight attached to different issues by different agents. It is assumed that if the value of an issue increases, that this is positive for one agent, and negative for the other. No assumptions about relation between time, negotiation history and issue-weight are required, in contrast to Bayesian learning. The difference between concessive (counter)offers is used to estimate the weights of the issues (assumption: stronger concessions are made later on in the negotiation). Faratin's hill climbing algorithm augmented with KDE is used to propose the next bid. KDE proved succesful on the used negotiation model. Future works entails testing the approach against different opponent strategies and extending the approach to other negotiation models (see assumption in summary).
Relevance	9. KDE learning described in detail. Strong related work section
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)	The ANAC competition models bilateral multi-issue closed negotiations and provides a benchmark for negotiation agents. Opponent models can also be used to identify the type of strategy of the opponent. Interesting agents for further analysis are: IAM(crazy)Haggler, FSEGA (profile learning), and Agent Smith. Issues can be predicatable, which means that they have a logical order, or unpredicatable, such as colors. This paper also includes acceptance conditions.
Summary
Relevance
Bibtex	​Link

Meetings

Planning