Version 117 (modified by 13 years ago) ( diff ) | ,
---|
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.
For an overview of the current status, see attached PDF
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 | Yes |
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 learning. Each value in an issue is imagined as approximating one of the three basic functions (downhill, uphill, triangular). Using the Bayesian formula, each hypothesis for a value is updated. Finally the hypothesis are combined based on their likelihood to determine the final form of the utility function for each value in the issue; combining these results in the utility function for an issue. Finally, the bidding strategy uses isocurves and the opponent model to increase acceptance. |
Relevance | 8 |
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 | Yes |
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 | Yes |
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 | Yes |
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 | Yes |
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 the dynamics of negotiation based on an analysis of move types. Boulware is a hard bargaining tactic, whereas conceder is soft. Besides evaluating the outcome one should also analyze the dance. This can be done by classifying the moves (nice, selfish, etc). A trace is a list of bids. The percentage of a type of move can be calculated. The sensitivy for opponent moves is based on this measure. |
Relevance | 7, interesting technique for evaluating strategies |
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) | 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 can we be sure that he is impartial? The negotiation strategy discussed is for billeteral multi-issue negotiation. A decreasing util curve is considered. A bid is calculated to fit the current util. Each issue has a seperate parameter such that more or less concession can be made on certain issues. General tolerance determines the general speed of concession. For each issue for the opponent bid and new calculated bid it is considered how much concession is made towards the opponent bid based on the configuration tolerance for each issue. This full formula depends on the weights of the opponent, which have to be estimated. The weights for each attribute can be estimated by comparing the distance between attributes for an issue in sequential bids and using this distance to mark the importance of an attribute. This last step is domain dependent. Concluding, the technique works, but requires tuning for the domain 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 | Link |
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 |
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 |
Processed | 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) | 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 other in a negotiation. A standard bileteral multi-lateral negotiation model is used. The issues are continious between a given range. Three types of techniques are considered: time-dependent, resource- dependent, and behaviour dependent. The results give an nice overview on which tactic is effective against which 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 | Link |
Cites seen | Yes |
Processed | Yes |
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) | ANAC, Pareto search, Bayes' rule |
Summary | IAMhaggler first determines the discount factor of the opponent by using non-linear regression. Next, the found curve is discounted to find the opponent bid curve. Next, the maximum is found on the opponent curve, and an appropriate curve is plotted for the own utility curve. For domains without unordered issues Pareto-search is used to determine all possible bids matching an utility. Next, it is determined which bid is the closest to the best received opponent bid by using the euclidean distance. For domains with unordered issues, each unorderded value is varied, after which the possible bids are determined which satisfy the utility. Finally, using Bayes' rule for opponent modelling, the best possible bid for the opponent is chosen. |
Relevance | 8, beautifull strategy |
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 | Yes |
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) | 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 the space where an agreement is possible. Each turn there is a negotiation between all agents to find a common space, which means that the agent recommunicate a refined space of agreement until an agreement is reached. The proces continues until a common decision is found (a decision is an element in the space of agreement). A learning algorithm can be used as follows: first the full domain space is split into zones, which are allocated a uniform chance. This chance is updated for each region for each agent based on the received space of agreement. When agents do not agree about the space, then a the space is chosen 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 | 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) | 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 results are not promosing. By limiting the amount exchanges, opponent models become more important, and lead to beter outcomes. |
Relevance | 2, since the paper is not specific enough |
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 | Yes |
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) | Nice Mirroring Strategy using Bayesian Learning |
Summary | Opponent models can aid in preventing exploitation, by determining the type of move of the opponent (selfish, unfortunate, concession, nice, fortunate, silent), and by taking the opponents preferences into account to increase the chance of acceptation. The mirror strategy mirrors the behaviours of the opponent, based on a classification of the opponent move. Nice MS does the same, but adds a nice move, which is a move which only increases the opponents utility without decreasing ours. Overall the strategy is shown to be effective by comparing the result of first testing the strategy against a random agent, and then the other agents. Also, the distance to a Kalai-Smorodinsky solution and the distance to the Nash Point is used as a metric. For future work the exploitability of MS should be researched. |
Relevance | 8, interesting application of opponent modelling |
Bibtex | Link |
Cites seen | Yes |
Processed | 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) | 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. Details can not be found in their paper, however this is not relevant for this survey. They also measures time by averaging over all bids. This is used to determine when to accept in panic. |
Relevance | 4, only learning the amount of rounds is interesting, but obvious |
Bibtex | X |
Cites seen | Yes |
Meetings
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.
Download all attachments as: .zip
Note:
See TracWiki
for help on using the wiki.