== 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||
||'''Cited'''||-||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:Snu0uoLL6tgJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||A Framework for Multi-agent Electronic Marketplaces: Analysis and Classification of Existing Systems ||
||'''Author(s)'''||K. Kurbel and I. Loutchko||
||'''Cited'''||25||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:rVdFnqvBOAMJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]
 
||'''Title'''||A Machine Learning Approach to Automated Negotiation and Prospects for Electronic Commerce ||
||'''Author(s)'''||J.R. Oliver||
||'''Cited'''||198||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:68RpIHxdsQEJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||AgentFSEGA - Time Constrained Reasoning Model for Bilateral Multi-Issue Negotiation||
||'''Author(s)'''||L.D. Serban, G.C. Silaghi, and C.M. Litan||
||'''Cited'''||-||
||'''Subject(s)'''||Learning issue utility curves by Bayesian learning; Learning issue ordering||
||'''Summary'''||The opponent model of FSEGA tries to approximate the ordering of the issues and the utility function of each issue by using Bayesian[[BR]] learning. Each value in an issue is imagined as approximating one of the three basic functions (downhill, uphill, triangular). Using[[BR]] the Bayesian formula, each hypothesis for a value is updated. Finally the hypothesis are combined based on their likelihood[[BR]] to determine the final form of the utility function for each value in the issue; combining these results in the utility function for[[BR]] an issue. Finally, the bidding strategy uses isocurves and the opponent model to increase acceptance.||
||'''Relevance'''||8||
||'''Bibtex'''||X||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Agents that Acquire Negotiation Strategies Using a Game Theoretic Learning Theory||
||'''Author(s)'''||N. Eiji Nawa||
||'''Cited'''||2||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:wGEmownS05MJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||An Adaptive Bilateral Negotiation Model for E-Commerce Settings||
||'''Author(s)'''||V. Narayanan and N.R. Jennings||
||'''Cited'''||26||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:-2t4LW-LK3cJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||An Adaptive Learning Method in Automated Negotiation Based on Artificial Neural Network||
||'''Author(s)'''||Z. Zeng, B. Meng, Y. Zeng||
||'''Cited'''||4||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:SaHRG-BD0RAJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||An Architecture for Negotiating Agents that Learn||
||'''Author(s)'''||H.H. Bui, S. Venkatesh, and D. Kieronska||
||'''Cited'''||2||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:1dvCOIJaG9cJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||An Automated Agent for Bilateral Negotiation with Bounded Rational Agents with Incomplete Information||
||'''Author(s)'''||R. Lin, S. Kraus, J. Wilkenfeld, J. Barry||
||'''Cited'''||23||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:vhcnBvl6XnMJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=00 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||An Evolutionairy Learning Approach for Adaptive Negotiation Agents||
||'''Author(s)'''||R.Y.K. Lau, M. Tang, O. Wong, S.W. Milliner||
||'''Cited'''||19||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:WQAKMsZjXk8J:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Analysis of Negotiation Dynamics||
||'''Author(s)'''||K. Hindriks, C.M. Jonker, D. Tykhonov||
||'''Cited'''||5||
||'''Subject(s)'''||Strategy evaluation||
||'''Summary'''||The process of the negotiation determines the outcome. This work presents an outline of a formal toolbox to analyze [[BR]]the dynamics of negotiation based on an analysis of move types. Boulware is a hard bargaining tactic, whereas [[BR]]conceder is soft. Besides evaluating the outcome one should also analyze the dance. This can be done by [[BR]]classifying the moves (nice, selfish, etc). A trace is a list of bids. The percentage of a type of move can be [[BR]]calculated. The sensitivy for opponent moves is based on this measure.  ||
||'''Relevance'''||7, interesting technique for evaluating strategies||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:p9dR-WdVTQAJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
||'''Processed'''||Yes||
[[BR]]

||'''Title'''||Anticipating Agent's Negotiation Strategies in an E-marketplace Using Belief Models||
||'''Author(s)'''||F. Teuteberg, K. Kurbel||
||'''Cited'''||11||
||'''Subject(s)'''||||
||'''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'''||Yes||
[[BR]]

||'''Title'''||Automated Multi-Attribute Negotiation with Efficient Use of Incomplete Preference Information||
||'''Author(s)'''||C. Jonker and V. Robu||
||'''Cited'''||44||
||'''Subject(s)'''||Mechanism for taking learning and initial information into account in a standard bilateral negotiation model||
||'''Summary'''||The classic technique for negotiation with undisclosed preferences is to use a mediator, however[[BR]] can we be sure that he is impartial? The negotiation strategy discussed is for billeteral multi-issue[[BR]] negotiation. A decreasing util curve is considered. A bid is calculated to fit the current [[BR]]util. Each issue has a seperate parameter such that more or less concession can be made on [[BR]]certain issues. General tolerance determines the general speed of concession. For each issue[[BR]] for the opponent bid and new calculated bid it is considered how much concession is made [[BR]]towards the opponent bid based on the configuration tolerance for each issue. This full [[BR]]formula depends on the weights of the opponent, which have to be estimated. The weights for each [[BR]]attribute can be estimated by comparing the distance between attributes for an issue in [[BR]]sequential bids and using this distance to mark the importance of an attribute. This last [[BR]]step is domain dependent. Concluding, the technique works, but requires tuning for the domain[[BR]] and assumes that the other agent plays a more or less similiar concession based technique. ||
||'''Relevance'''||4, domain dependent opponent modelling approach for learning ordering of attributes||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:fSLXt9dFf4kJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
||'''Processed'''||Yes||
[[BR]]

||'''Title'''||Bayesian Learning in Bilateral Multi-issue Negotiation and its Application in MAS-based Electronic Commerce||
||'''Author(s)'''||J. Li, Y. Cao||
||'''Cited'''||6||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:QuJqFn4TJaAJ: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||
||'''Cited'''||355||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:OcrAgrlmKdgJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
||'''Processed'''||Yes||
[[BR]]

||'''Title'''||Benefits of Learning in Negotiation||
||'''Author(s)'''||D. Zeng, K. Sycara||
||'''Cited'''||116||
||'''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||
||'''Processed'''||Yes||
[[BR]]

||'''Title'''||Bilateral Negotiation with Incomplete and Uncertain Information: A Decision-Theoretic Approach Using a Model of the Opponent||
||'''Author(s)'''||C. Mudgal, J. Vassileva||
||'''Cited'''||42||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Building Automated Negotiation Strategies Enhanced by MLP and GR Neural Networks for Opponent Agent Behaviour Prognosis||
||'''Author(s)'''||I. Roussaki, I. Papaioannou, and M. Anagostou||
||'''Cited'''||3||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:18qiNH2UInwJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Comparing Equilibria for Game-Theoretic and Evolutionary Bargaining Models||
||'''Author(s)'''||S. Fatima, M. Wooldridge, N.R. Jennings||
||'''Cited'''||21||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:aLP4CeRMh68J:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Compromising Strategy based on Estimated Maximum Utility for Automated Negotiating Agents||
||'''Author(s)'''||S. Kawaguchi, K. Fujita, T. Ito||
||'''Cited'''||-||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||X||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Determining Succesful Negotiation Strategies: An Evolutionary Approach||
||'''Author(s)'''||N. Matos, C. Sierra, N.R. Jennings||
||'''Cited'''||149||
||'''Subject(s)'''||Analysing strengths and weakness of tactics||
||'''Summary'''||This work uses an evolutionary approach to find on how agents using particular negotiation strategies fare against each[[BR]] other in a negotiation. A standard bileteral multi-lateral negotiation model is used. The issues are continious[[BR]] between a given range. Three types of techniques are considered: time-dependent, resource-[[BR]]dependent, and behaviour dependent. The results give an nice overview on which tactic is effective against which [[BR]]opponent. This is very interesting if the opponent is somehow able to determine the type of agent.||
||'''Relevance'''||8, motivation for learning of strategies||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:jAWqPD9IQ-sJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
||'''Processed'''||Yes||
[[BR]]

||'''Title'''||Facing the Challenge of Human-Agent Negotiations via Effective General Opponent Modeling||
||'''Author(s)'''||Y. Oshrat, R. Lin, S. Kraus||
||'''Cited'''||19||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:D7XLjMbCgQkJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Genetic Algorithms for Automated Negotiations: A FSM-Based Application Approach||
||'''Author(s)'''||M.T. Tu, E. Wolff, W. Lamersdorf||
||'''Cited'''||37||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:kO_zImqufQMJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||IAMhaggler: A Negotiation Agent for Complex Environments||
||'''Author(s)'''||C.R. Williams, V. Robu, E.H. Gerding, and N.R. Jennings||
||'''Cited'''||-||
||'''Subject(s)'''||ANAC, Pareto search, Bayes' rule||
||'''Summary'''||IAMhaggler first determines the discount factor of the opponent by using non-linear regression. Next, the found curve[[BR]] is discounted to find the opponent bid curve. Next, the maximum is found on the opponent curve, and an [[BR]] appropriate curve is plotted for the own utility curve. For domains without unordered issues Pareto-search is [[BR]] used to determine all possible bids matching an utility. Next, it is determined which bid is the closest to the best[[BR]]  received opponent bid by using the euclidean distance. For domains with unordered issues, each [[BR]] unorderded value is varied, after which the possible bids are determined which satisfy the utility. Finally, using Bayes' [[BR]] rule for opponent modelling, the best possible bid for the opponent is chosen. ||
||'''Relevance'''||8, beautifull strategy||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:dXychgQCiFMJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Inferring implicit preferences from negotiation actions||
||'''Author(s)'''||A. Restificar and P. Haddawy||
||'''Cited'''||10||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:m6_7yTkcPBwJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Integration of Learning, Situational Power and Goal Constraints Into Time-Dependent Electronic Negotiation Agents||
||'''Author(s)'''||W.W.H. Mok||
||'''Cited'''||-||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:58qYQ6xl6vgJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=6 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Learning Algorithms for Single-instance Electronic Negotiations using the Time-dependent Behavioral Tactic||
||'''Author(s)'''||W.W.H Mok and R.P. Sundarraj||
||'''Cited'''||17||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:9mOSw0JyumEJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Learning an Agent's Utility Function by Observing Behavior||
||'''Author(s)'''||U. Chajewska, D. Koller, D. Ormoneit||
||'''Cited'''||54||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:JXqC1SLmlPIJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]


||'''Title'''||Learning an Opponent's Preferences to Make Effective Multi-Issue Negotiation Trade-Offs||
||'''Author(s)'''||R.M. Coehoorn, N.R. Jennings||
||'''Cited'''||78||
||'''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 Opponents' Preferences in Multi-Object Automated Negotiation||
||'''Author(s)'''||S. Buffett and B. Spencer||
||'''Cited'''||18||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:3k4MYX9X6BcJ: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 using the Bayesian Classifier.||
||'''Author(s)'''||H.H. Bui, D. Kieronska, S. Venkatesh||
||'''Cited'''||29||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:zJTcBPpxfYoJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Learning to Select Negotiation Strategies in Multi-Agent Meeting Scheduling||
||'''Author(s)'''||E. Crawford and M. Veleso||
||'''Cited'''||21||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:PfWgZlbnox8J:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Modelling Agents Behaviour in Automated Negotiation||
||'''Author(s)'''||C. Hou||
||'''Cited'''||10||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:OznI0-O4SlgJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Modeling Opponent Decision in Repeated One-shot Negotiations||
||'''Author(s)'''||S.Saha, A. Biswas, S. Sen||
||'''Cited'''||26||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:HWyIIq6nlNUJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Negotiating agents that learn about others' preferences||
||'''Author(s)'''||H.H. Bui, D. Kieronska and S. Venkatesh||
||'''Cited'''||5||
||'''Subject(s)'''||Logic-like representation negotiation model, bin-based opponent model, one-issue continious||
||'''Summary'''||The used method assumes that domain knowledge is available to partition the search space. Each turns, the agents communicate [[br]]the space where an agreement is possible. Each turn there is a negotiation between all agents to find a common space, which [[br]]means that the agent recommunicate a refined space of agreement until an agreement is reached. The proces continues until [[br]]a common decision is found (a decision is an element in the space of agreement). A learning algorithm can be used as follows: [[br]]first the full domain space is split into zones, which are allocated a uniform chance. This chance is updated for each region [[br]]for each agent based on the received space of agreement. When agents do not agree about the space, then a the space is chosen [[br]]which has the maximum support based on the chances of each space for each agent. This leads to a higher chance of agreement.
||
||'''Relevance'''||3, domain knowledge required and only considers one issue||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:8EOwrOyBdv0J:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Negotiation Decision Functions for Autonomous Agent||
||'''Author(s)'''||P. Faratin, C. Sierra, N.R. Jennings||
||'''Cited'''||718||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:Pmj4ztkTFq4J:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Negotiation Dynamics: Analysis, Concession Tactics, and Outcomes||
||'''Author(s)'''||K. Hindriks, C.M. Jonker, D. Tykhonov||
||'''Cited'''||7||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:8lUoyWRsIMMJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||On-Line Incremental Learning in Bilateral Multi-Issue Negotiation||
||'''Author(s)'''||V. Soo, C. Hung||
||'''Cited'''||18||
||'''Subject(s)'''||Online incremental learning using neural networks||
||'''Summary'''||This paper discusses using neural networks for learning the opponent model, however it is not described how, and the[[BR]] results are not promosing. By limiting the amount exchanges, opponent models become more important, and [[BR]]lead to beter outcomes.
||
||'''Relevance'''||2, since the paper is not specific enough||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:G3nExE5HdskJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||On Learning Negotiation Strategies by Artificial Adaptive Agents in Environments of Incomplete Information||
||'''Author(s)'''||J.R. Oliver||
||'''Cited'''||6||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:zk0BU_aG2ZIJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Opponent Model Estimation in Bilateral Multi-issue Negotiation||
||'''Author(s)'''||N. van Galen Last||
||'''Cited'''||-||
||'''Subject(s)'''||Agent which participated in ANAC2010||
||'''Summary'''||Overall not interesting, but encouraged me to find fields involved in negotiation.||
||'''Relevance'''||2||
||'''Bibtex'''||X||
||'''Cites seen'''||Yes||
||'''Processed'''||Yes||
[[BR]]

||'''Title'''||Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning||
||'''Author(s)'''||K. Hindriks, D. Tykhonov||
||'''Cited'''||33||
||'''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'''||Optimal negotiation strategies for agents with incomplete information||
||'''Author(s)'''||S.S. Fatima, M. Wooldridge and N.R. Jennings||
||'''Cited'''||88||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:zfXBf6ObIaEJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Predicting Agents Tactics in Automated Negotiation||
||'''Author(s)'''||C. Hou||
||'''Cited'''||12||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:4CP7PDlOJL8J:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Predicting partner's behaviour in agent negotiation||
||'''Author(s)'''||J. Brzostowski, R. Kowalczyk||
||'''Cited'''||16||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:3MifmJepz_4J: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||
||'''Cited'''||-||
||'''Subject(s)'''||Nice Mirroring Strategy using Bayesian Learning||
||'''Summary'''||Opponent models can aid in preventing exploitation, by determining the type of move of the opponent (selfish, [[BR]] unfortunate, concession, nice, fortunate, silent), and by taking the opponents preferences into account to[[BR]] increase the chance of acceptation. The mirror strategy mirrors the behaviours of the opponent, based on a [[BR]]classification of the opponent move. Nice MS does the same, but adds a nice move, which is a move which only [[BR]]increases the opponents utility without decreasing ours. Overall the strategy is shown to be effective by [[BR]]comparing the result of first testing the strategy against a random agent, and then the other agents. Also, the[[BR]] distance to a Kalai-Smorodinsky solution and the distance to the Nash Point is used as a metric. For [[BR]]future work the exploitability of MS should be researched.||
||'''Relevance'''||8, interesting application of opponent modelling ||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:BtssqMir4RcJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=2 Link]||
||'''Cites seen'''||Yes||
||'''Processed'''||Yes||
[[BR]]

||'''Title'''||The First Automated Negotiating Agents Competition (ANAC 2010)||
||'''Author(s)'''||T. Baarslag, K. Hindriks, C. Jonker, S. Kraus, R. Lin||
||'''Cited'''||-||
||'''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'''||Yes||
[[BR]]

||'''Title'''||Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation||
||'''Author(s)'''||K.V. Hindriks and D. Tykhonov||
||'''Cited'''||6||
||'''Subject(s)'''||Measures for quality of opponent model||
||'''Summary'''||See section on quality measures in paper||
||'''Relevance'''||9||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:oxFpfvvuE94J:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
||'''Processed'''||Yes||
[[BR]]

||'''Title'''||Using Similarity Criteria to Make Issue Trade-offs in Automated Negotiations||
||'''Author(s)'''||P. Faratin, C. Sierra, N.R. Jennings||
||'''Cited'''||367||
||'''Subject(s)'''||||
||'''Summary'''||||
||'''Relevance'''||||
||'''Bibtex'''||[http://scholar.google.nl/scholar.bib?q=info:xXqv_X0tP9MJ:scholar.google.com/&output=citation&hl=nl&as_sdt=0,5&ct=citation&cd=0 Link]||
||'''Cites seen'''||Yes||
[[BR]]

||'''Title'''||Yushu: a Heuristic-Based Agent for Automated Negotiating Competition||
||'''Author(s)'''||B. An and V. Lesser||
||'''Cited'''||-||
||'''Subject(s)'''||ANAC agent, Complexity learning||
||'''Summary'''||One of the interesting strategies of Yushu is that it tries to measure the competitiveness, which influences it's bidding strategy.[[BR]] Details can not be found in their paper, however this is not relevant for this survey. They also measures time by averaging [[BR]] over all bids. This is used to determine when to accept in panic.||
||'''Relevance'''||4, only finding the amount of rounds is interesting, but obvious||
||'''Bibtex'''||X||
||'''Cites seen'''||Yes||
||'''Processed'''||Yes||
[[BR]]