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.
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Papers

Title	A Framework for Building Intelligent SLA Negotiation Strategies under Time Constraints
Author(s)	G.C. Silaghi, L.D. Şerban and C.M. Litan
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Title	A Framework for Multi-agent Electronic Marketplaces: Analysis and Classification of Existing Systems
Author(s)	K. Kurbel and I. Loutchko
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Title	AgentFSEGA - Time Constrained Reasoning Model for Bilateral Multi-Issue Negotiation
Author(s)	L.D. Serban, G.C. Silaghi, and C.M. Litan
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Title	An Architecture for Negotiating Agents that Learn
Author(s)	H.H. Bui, S. Venkatesh, and D. Kieronska
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Cites seen	Yes

Title	Analysis of Negotiation Dynamics
Author(s)	K. Hindriks, C.M. Jonker, D. Tykhonov
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Title	Anticipating Agent's Negotiation Strategies in an E-marketplace Using Belief Models
Author(s)	F. Teuteberg, K. Kurbel
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Cites seen	Yes

Title	Bayesian Learning in Bilateral Multi-issue Negotiation and its Application in MAS-based Electronic Commerce
Author(s)	J. Li, Y. Cao
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Cites seen	Yes

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
Cites seen	Yes

Title	Bayesian Learning in Negotiation
Author(s)	D. Zeng, K. Sycara
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Title	Bilateral Negotiation with Incomplete and Uncertain Information
Author(s)	C. Mudgal, J. Vassileva
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Title	Compromising Strategy based on Estimated Maximum Utility for Automated Negotiating Agents
Author(s)	S. Kawaguchi, K. Fujita, T. Ito
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Bibtex	X
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Title	Using Similarity Criteria to Make Issue Trade-offs in Automated Negotiations
Author(s)	P. Faratin, C. Sierra, N.R. Jennings
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Title	Facing the Challenge of Human-Agent Negotiations via Effective General Opponent Modeling
Author(s)	J. Li, Y. Cao
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Title	IAMhaggler: A Negotiation Agent for Complex Environments
Author(s)	C.R. Williams, V. Robu, E.H. Gerding, and N.R. Jennings
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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
Cites seen	Yes

Title	Learning other agents' preferences in multiagent negotiation
Author(s)	H.H. Bui, D. Kieronska, S. Venkatesh
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Cites seen	Yes

Title	Negotiation Decision Functions for Autonomous Agent
Author(s)	P. Faratin, C. Sierra, N.R. Jennings
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Title	On-Line Incremental Learning in Bilateral Multi-Issue Negotiation
Author(s)	V. Soo, C. Hung
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Cites seen	Yes

Title	Opponent Model Estimation in Bilateral Multi-issue Negotiation
Author(s)	N. van Galen Last
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Bibtex	X
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Title	Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning
Author(s)	K. Hindriks, D. Tykhonov
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Cites seen	Yes

Title	The Benefits of Opponent Models in Negotiation
Author(s)	K. Hindriks, C.M. Jonker, D. Tykhonov
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Cites seen	Yes

Title	The First Automated Negotiating Agents Competition (ANAC 2010)
Author(s)	T. Baarslag, K. Hindriks, C. Jonker, S. Kraus, R. Lin
Subject(s)	ANAC, overview multiple agents, opponent models, acceptance conditions
Summary	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.
Relevance	5, too global, however interesting citations
Bibtex	​Link
Cites seen

Title	Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation
Author(s)	K.V. Hindriks and D. Tykhonov
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Title	Yushu: a Heuristic-Based Agent for Automated Negotiating Competition
Author(s)	B. An and V. Lesser
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Bibtex	X
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Meetings

Onaangekondigde Meeting (20/04/11)

In verband met de conferentie in Taiwan is in overleg met Tim besproken dat we waarschijnlijk meer tijd nodig gaan hebben voor ons paper.
Dit houdt in dat de deadline is verschoven naar begin september. Als persoonlijk doel heb ik gesteld om eind juni een kladversie te hebben.

Planning

26/04/11	Meeste interessante papers gevonden
28/04/11 - 09/05-11	Conferentie bijwonen en op laag tempo doorwerken
23/05/11	Alle papers samengevat
26/05/11	Opzet taxonomie
29/06/11	Kladversie paper af