= 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[[br]]of the relevant details. The second Section provides an overview of all meetings. Finally, the third Section outlines a global planning.


== Papers ==

||'''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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||'''Summary'''||||
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[[BR]]

||'''Title'''||An Architecture for Negotiating Agents that Learn||
||'''Author(s)'''||H.H. Bui, S. Venkatesh, and D. Kieronska||
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||'''Summary'''||||
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[[BR]]

||'''Title'''||Analysis of Negotiation Dynamics||
||'''Author(s)'''||K. Hindriks, C.M. Jonker, D. Tykhonov||
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||'''Summary'''||||
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||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Anticipating Agent's Negotiation Strategies in an E-marketplace Using Belief Models||
||'''Author(s)'''||F. Teuteberg, K. Kurbel||
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||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:qauKvN1Swx8J:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||||
[[BR]]

||'''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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||'''Summary'''||||
||'''Relevance'''||||
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||'''Cites seen'''||Yes||
[[BR]]

||'''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[[br]] emerging. In contrast to most negotiation models, sequential decision model allows for learning. Learning can help understand[[br]] human behaviour, but can also result in better results for the learning party. Bayesian learning of reservation[[br]] values can be used to determine the zone of agreement for an issue based on the domain knowledge and bidding interactions.[[br]] Concluding for one-issue, learning positively influences bargaining quality, number of exchanged proposals,[[br]] 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'''||[http://scholar.google.nl/scholar.bib?q=info:omTF-8TbGE4J:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Bayesian Learning in Negotiation||
||'''Author(s)'''||D. Zeng, K. Sycara||
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[[BR]]

||'''Title'''||Bilateral Negotiation with Incomplete and Uncertain Information||
||'''Author(s)'''||C. Mudgal, J. Vassileva||
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||'''Summary'''||||
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||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Compromising Strategy based on Estimated Maximum Utility for Automated Negotiating Agents||
||'''Author(s)'''||S. Kawaguchi, K. Fujita, T. Ito||
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[[BR]]

||'''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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||'''Summary'''||||
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[[BR]]

||'''Title'''||Facing the Challenge of Human-Agent Negotiations via Effective General Opponent Modeling||
||'''Author(s)'''||J. Li, Y. Cao||
||'''Subject(s)'''||||
||'''Summary'''||||
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[[BR]]

||'''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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[[BR]]

||'''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 [[br]]over the issues in the domain. Kernel Density Estimation (KDE) is used to estimate the weight attached to different issues [[br]]by different agents. It is assumed that if the value of an issue increases, that this is positive for one agent, and negative [[br]]for the other. No assumptions about relation between time, negotiation history and issue-weight are required, in contrast [[br]]to Bayesian learning. The difference between concessive (counter)offers is used to estimate the weights of the issues [[br]] (assumption: stronger concessions are made later on in the negotiation). Faratin's hill climbing algorithm augmented with KDE is [[br]]used to propose the next bid. KDE proved succesful on the used negotiation model. Future works entails testing the approach [[br]]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'''||[http://scholar.google.nl/scholar.bib?q=info:Z79P04-IRS0J:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Learning other agents' preferences in multiagent negotiation||
||'''Author(s)'''||H.H. Bui, D. Kieronska, S. Venkatesh||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Negotiation Decision Functions for Autonomous Agent||
||'''Author(s)'''||P. Faratin, C. Sierra, N.R. Jennings||
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[[BR]]

||'''Title'''||On-Line Incremental Learning in Bilateral Multi-Issue Negotiation||
||'''Author(s)'''||V. Soo, C. Hung||
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||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Opponent Model Estimation in Bilateral Multi-issue Negotiation||
||'''Author(s)'''||N. van Galen Last||
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[[BR]]

||'''Title'''||Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning||
||'''Author(s)'''||K. Hindriks, D. Tykhonov||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:BtssqMir4RcJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||The Benefits of Opponent Models in Negotiation||
||'''Author(s)'''||K. Hindriks, C.M. Jonker, D. Tykhonov||
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||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||||
||'''Cites seen'''||Yes||
[[BR]]

||'''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. [[br]]Opponent models can also be used to identify the type of strategy of the opponent. Interesting agents for further analysis [[br]]are: IAM(crazy)Haggler, FSEGA (profile learning), and Agent Smith. Issues can be predicatable, which means that they [[br]]have a logical order, or unpredicatable, such as colors. This paper also includes acceptance conditions.||
||'''Relevance'''||5, too global, however interesting citations
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:vKSG_Lm38D0J:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=3 Link]||
||'''Cites seen'''|||||
[[BR]]

||'''Title'''||Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation||
||'''Author(s)'''||K.V. Hindriks and D. Tykhonov||
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||'''Summary'''||||
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||'''Cites seen'''||||
[[BR]]

||'''Title'''||Yushu: a Heuristic-Based Agent for Automated Negotiating Competition||
||'''Author(s)'''||B. An and V. Lesser||
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[[BR]]

== Meetings ==

=== Onaangekondige 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.[[BR]]
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||