Version 125 (modified by mark, 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

TitleA 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
BibtexLink
Cites seenYes


TitleA Framework for Multi-agent Electronic Marketplaces: Analysis and Classification of Existing Systems
Author(s)K. Kurbel and I. Loutchko
Cited25
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleA Machine Learning Approach to Automated Negotiation and Prospects for Electronic Commerce
Author(s)J.R. Oliver
Cited198
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleAgentFSEGA - 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
SummaryThe 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.
Relevance8
BibtexX
Cites seenYes


TitleAgents that Acquire Negotiation Strategies Using a Game Theoretic Learning Theory
Author(s)N. Eiji Nawa
Cited2
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleAn Adaptive Bilateral Negotiation Model for E-Commerce Settings
Author(s)V. Narayanan and N.R. Jennings
Cited26
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleAn Adaptive Learning Method in Automated Negotiation Based on Artificial Neural Network
Author(s)Z. Zeng, B. Meng, Y. Zeng
Cited4
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleAn Architecture for Negotiating Agents that Learn
Author(s)H.H. Bui, S. Venkatesh, and D. Kieronska
Cited2
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleAn Automated Agent for Bilateral Negotiation with Bounded Rational Agents with Incomplete Information
Author(s)R. Lin, S. Kraus, J. Wilkenfeld, J. Barry
Cited23
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleAn Evolutionairy Learning Approach for Adaptive Negotiation Agents
Author(s)R.Y.K. Lau, M. Tang, O. Wong, S.W. Milliner
Cited19
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleAnalysis of Negotiation Dynamics
Author(s)K. Hindriks, C.M. Jonker, D. Tykhonov
Cited5
Subject(s)Strategy evaluation
SummaryThe 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.
Relevance7, interesting technique for evaluating strategies
BibtexLink
Cites seenYes
ProcessedYes


TitleAnticipating Agent's Negotiation Strategies in an E-marketplace Using Belief Models
Author(s)F. Teuteberg, K. Kurbel
Cited11
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleAutomated Multi-Attribute Negotiation with Efficient Use of Incomplete Preference Information
Author(s)C. Jonker and V. Robu
Cited44
Subject(s)Mechanism for taking learning and initial information into account in a standard bilateral negotiation model
SummaryThe 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.
Relevance4, domain dependent opponent modelling approach for learning ordering of attributes
BibtexLink
Cites seenYes
ProcessedYes


TitleBayesian Learning in Bilateral Multi-issue Negotiation and its Application in MAS-based Electronic Commerce
Author(s)J. Li, Y. Cao
Cited6
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleBayesian Learning in Negotiation
Author(s)D. Zeng, K. Sycara
Cited355
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleBenefits of Learning in Negotiation
Author(s)D. Zeng, K. Sycara
Cited116
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.
Relevance8. Strong example of Bayesian learning
BibtexLink
Cites seenYes
ProcessedYes


TitleBilateral Negotiation with Incomplete and Uncertain Information: A Decision-Theoretic Approach Using a Model of the Opponent
Author(s)C. Mudgal, J. Vassileva
Cited42
Subject(s)
Summary
Relevance
Bibtex
Cites seenYes


TitleBuilding Automated Negotiation Strategies Enhanced by MLP and GR Neural Networks for Opponent Agent Behaviour Prognosis
Author(s)I. Roussaki, I. Papaioannou, and M. Anagostou
Cited3
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleComparing Equilibria for Game-Theoretic and Evolutionary Bargaining Models
Author(s)S. Fatima, M. Wooldridge, N.R. Jennings
Cited21
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleCompromising Strategy based on Estimated Maximum Utility for Automated Negotiating Agents
Author(s)S. Kawaguchi, K. Fujita, T. Ito
Cited-
Subject(s)
Summary
Relevance
BibtexX
Cites seenYes


TitleDetermining Succesful Negotiation Strategies: An Evolutionary Approach
Author(s)N. Matos, C. Sierra, N.R. Jennings
Cited149
Subject(s)Analysing strengths and weakness of tactics
SummaryThis 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.
Relevance8, motivation for learning of strategies
BibtexLink
Cites seenYes
ProcessedYes


TitleFacing the Challenge of Human-Agent Negotiations via Effective General Opponent Modeling
Author(s)Y. Oshrat, R. Lin, S. Kraus
Cited19
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleGenetic Algorithms for Automated Negotiations: A FSM-Based Application Approach
Author(s)M.T. Tu, E. Wolff, W. Lamersdorf
Cited37
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleIAMhaggler: 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
SummaryIAMhaggler 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.
Relevance8, beautifull strategy
BibtexLink
Cites seenYes


TitleInferring implicit preferences from negotiation actions
Author(s)A. Restificar and P. Haddawy
Cited10
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleIntegration of Learning, Situational Power and Goal Constraints Into Time-Dependent Electronic Negotiation Agents
Author(s)W.W.H. Mok
Cited-
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleLearning Algorithms for Single-instance Electronic Negotiations using the Time-dependent Behavioral Tactic
Author(s)W.W.H Mok and R.P. Sundarraj
Cited17
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleLearning an Agent's Utility Function by Observing Behavior
Author(s)U. Chajewska, D. Koller, D. Ormoneit
Cited54
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleLearning an Opponent's Preferences to Make Effective Multi-Issue Negotiation Trade-Offs
Author(s)R.M. Coehoorn, N.R. Jennings
Cited78
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).
Relevance9. KDE learning described in detail. Strong related work section
BibtexLink
Cites seenYes


TitleLearning Opponents' Preferences in Multi-Object Automated Negotiation
Author(s)S. Buffett and B. Spencer
Cited18
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleLearning other Agents' Preferences in Multiagent Negotiation using the Bayesian Classifier.
Author(s)H.H. Bui, D. Kieronska, S. Venkatesh
Cited29
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleLearning to Select Negotiation Strategies in Multi-Agent Meeting Scheduling
Author(s)E. Crawford and M. Veleso
Cited21
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleModelling Agents Behaviour in Automated Negotiation
Author(s)C. Hou
Cited10
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleModeling Opponent Decision in Repeated One-shot Negotiations
Author(s)S.Saha, A. Biswas, S. Sen
Cited26
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleNegotiating agents that learn about others' preferences
Author(s)H.H. Bui, D. Kieronska and S. Venkatesh
Cited5
Subject(s)Logic-like representation negotiation model, bin-based opponent model, one-issue continious
SummaryThe 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.
Relevance3, domain knowledge required and only considers one issue
BibtexLink
Cites seenYes


TitleNegotiation Decision Functions for Autonomous Agent
Author(s)P. Faratin, C. Sierra, N.R. Jennings
Cited718
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleNegotiation Dynamics: Analysis, Concession Tactics, and Outcomes
Author(s)K. Hindriks, C.M. Jonker, D. Tykhonov
Cited7
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleOn-Line Incremental Learning in Bilateral Multi-Issue Negotiation
Author(s)V. Soo, C. Hung
Cited18
Subject(s)Online incremental learning using neural networks
SummaryThis 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.
Relevance2, since the paper is not specific enough
BibtexLink
Cites seenYes


TitleOn Learning Negotiation Strategies by Artificial Adaptive Agents in Environments of Incomplete Information
Author(s)J.R. Oliver
Cited6
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleOpponent Model Estimation in Bilateral Multi-issue Negotiation
Author(s)N. van Galen Last
Cited-
Subject(s)Agent which participated in ANAC2010
SummaryOverall not interesting, but encouraged me to find fields involved in negotiation.
Relevance2
BibtexX
Cites seenYes
ProcessedYes


TitleOpponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning
Author(s)K. Hindriks, D. Tykhonov
Cited33
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleOptimal negotiation strategies for agents with incomplete information
Author(s)S.S. Fatima, M. Wooldridge and N.R. Jennings
Cited88
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitlePredicting Agents Tactics in Automated Negotiation
Author(s)C. Hou
Cited12
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitlePredicting partner's behaviour in agent negotiation
Author(s)J. Brzostowski, R. Kowalczyk
Cited16
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleThe Benefits of Opponent Models in Negotiation
Author(s)K. Hindriks, C.M. Jonker, D. Tykhonov
Cited-
Subject(s)Nice Mirroring Strategy using Bayesian Learning
SummaryOpponent 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.
Relevance8, interesting application of opponent modelling
BibtexLink
Cites seenYes
ProcessedYes


TitleThe 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
SummaryThe 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.
Relevance5, too global, however interesting citations
BibtexLink
Cites seenYes


TitleTowards a Quality Assessment Method for Learning Preference Profiles in Negotiation
Author(s)K.V. Hindriks and D. Tykhonov
Cited6
Subject(s)Measures for quality of opponent model
SummarySee section on quality measures in paper
Relevance9
BibtexLink
Cites seenYes
ProcessedYes


TitleUsing Similarity Criteria to Make Issue Trade-offs in Automated Negotiations
Author(s)P. Faratin, C. Sierra, N.R. Jennings
Cited367
Subject(s)
Summary
Relevance
BibtexLink
Cites seenYes


TitleYushu: a Heuristic-Based Agent for Automated Negotiating Competition
Author(s)B. An and V. Lesser
Cited-
Subject(s)ANAC agent, Complexity learning
SummaryOne 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.
Relevance4, only finding the amount of rounds is interesting, but obvious
BibtexX
Cites seenYes
ProcessedYes


Meetings

Link

Planning

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

Attachments (3)

Download all attachments as: .zip

Note: See TracWiki for help on using the wiki.