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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 |
Cited | - |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
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 | Link |
Cites seen | Yes |
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 | Link |
Cites seen |
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) | |
Summary | |
Relevance | |
Bibtex | X |
Cites seen | Yes |
Title | Agents that Acquire Negotiation Strategies Using a Game Theoretic Learning Theory |
Author(s) | N. Eiji Nawa |
Cited | 2 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen |
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 | Link |
Cites seen | Yes |
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 | Link |
Cites seen |
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 | Link |
Cites seen | Yes |
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 | Link |
Cites seen |
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 | Link |
Cites seen |
Title | Analysis of Negotiation Dynamics |
Author(s) | K. Hindriks, C.M. Jonker, D. Tykhonov |
Cited | 5 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
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 | Link |
Cites seen | Yes |
Title | Automated Multi-Attribute Negotiation with Efficient Use of Incomplete Preference Information |
Author(s) | C. Jonker and V. Robu |
Cited | 44 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen |
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 | Link |
Cites seen | Yes |
Title | Bayesian Learning in Negotiation |
Author(s) | D. Zeng, K. Sycara |
Cited | 355 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
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 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 | 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 |
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 | Link |
Cites seen | Yes |
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 | Link |
Cites seen | Yes |
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 |
Title | Determining Succesful Negotiation Strategies: An Evolutionary Approach |
Author(s) | N. Matos, C. Sierra, N.R. Jennings |
Cited | 149 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen |
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 | Link |
Cites seen | Yes |
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 | Link |
Cites seen | Yes |
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) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
Title | Inferring implicit preferences from negotiation actions |
Author(s) | A. Restificar and P. Haddawy |
Cited | 10 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
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 | Link |
Cites seen | Yes |
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 | Link |
Cites seen | Yes |
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 | Link |
Cites seen | Yes |
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 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 Opponents' Preferences in Multi-Object Automated Negotiation |
Author(s) | S. Buffett and B. Spencer |
Cited | 18 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
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 | Link |
Cites seen | Yes |
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 | Link |
Cites seen |
Title | Modelling Agents Behaviour in Automated Negotiation |
Author(s) | C. Hou |
Cited | 10 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
Title | Modeling Opponent Decision in Repeated One-shot Negotiations |
Author(s) | S.Saha, A. Biswas, S. Sen |
Cited | 26 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
Title | Negotiating agents that learn about others' preferences |
Author(s) | H.H. Bui, D. Kieronska and S. Venkatesh |
Cited | 5 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
Title | Negotiation Decision Functions for Autonomous Agent |
Author(s) | P. Faratin, C. Sierra, N.R. Jennings |
Cited | 718 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
Title | Negotiation Dynamics: Analysis, Concession Tactics, and Outcomes |
Author(s) | K. Hindriks, C.M. Jonker, D. Tykhonov |
Cited | 7 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
Title | On-Line Incremental Learning in Bilateral Multi-Issue Negotiation |
Author(s) | V. Soo, C. Hung |
Cited | 18 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
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 | Link |
Cites seen |
Title | Opponent Model Estimation in Bilateral Multi-issue Negotiation |
Author(s) | N. van Galen Last |
Cited | - |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | X |
Cites seen | Yes |
Title | Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning |
Author(s) | K. Hindriks, D. Tykhonov |
Cited | 33 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
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 | Link |
Cites seen | Yes |
Title | Predicting Agents Tactics in Automated Negotiation |
Author(s) | C. Hou |
Cited | 12 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
Title | Predicting partner's behaviour in agent negotiation |
Author(s) | J. Brzostowski, R. Kowalczyk |
Cited | 16 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
Title | The Benefits of Opponent Models in Negotiation |
Author(s) | K. Hindriks, C.M. Jonker, D. Tykhonov |
Cited | - |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
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. 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 | Yes |
Title | Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation |
Author(s) | K.V. Hindriks and D. Tykhonov |
Cited | 6 |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | Link |
Cites seen | Yes |
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 | Link |
Cites seen | Yes |
Title | Yushu: a Heuristic-Based Agent for Automated Negotiating Competition |
Author(s) | B. An and V. Lesser |
Cited | - |
Subject(s) | |
Summary | |
Relevance | |
Bibtex | X |
Cites seen | Yes |
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 |
Attachments (3)
- OM_lit.bib (25.4 KB ) - added by 13 years ago.
- OM_lit.2.bib (25.3 KB ) - added by 13 years ago.
- OM_lit.3.bib (25.0 KB ) - added by 13 years ago.
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