Rules of encounter: designing conventions for automated negotiation among computers
Rules of encounter: designing conventions for automated negotiation among computers
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Learning on opponent's preferences to make effective multi-issue negotiation trade-offs
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Human vs. Computer Behaviour in Multi-Issue Negotiation
RRS '05 Proceedings of the Rational, Robust, and Secure Negotiation Mechanisms in Multi-Agent Systems (RRS'05) on Multi-Agent Systems
An agent architecture for multi-attribute negotiation using incomplete preference information
Autonomous Agents and Multi-Agent Systems
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Benefits of learning in negotiation
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Learning to negotiate optimally in non-stationary environments
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
Towards an Open Negotiation Architecture for Heterogeneous Agents
CIA '08 Proceedings of the 12th international workshop on Cooperative Information Agents XII
Facing the challenge of human-agent negotiations via effective general opponent modeling
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Using opponent models for efficient negotiation
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Approximating an auction mechanism by multi-issue negotiation
HuCom '08 Proceedings of the 1st International Working Conference on Human Factors and Computational Models in Negotiation
An agent model for the influence of culture on bargaining
HuCom '08 Proceedings of the 1st International Working Conference on Human Factors and Computational Models in Negotiation
Ontology-Based Learning for Negotiation
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
The Benefits of Opponent Models in Negotiation
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Affective negotiation support systems
Journal of Ambient Intelligence and Smart Environments
Model identification in interactive influence diagrams using mutual information
Web Intelligence and Agent Systems
Learning negotiation policies using IB3 and Bayesian networks
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
A framework for building intelligent SLA negotiation strategies under time constraints
GECON'10 Proceedings of the 7th international conference on Economics of grids, clouds, systems, and services
Eliminating issue dependencies in complex negotiation domains
Multiagent and Grid Systems - Advances in Agent-mediated Automated Negotiations
Detecting drifts in multi-issue negotiations
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
A study of computational and human strategies in revelation games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
The effect of expression of anger and happiness in computer agents on negotiations with humans
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
A multi-agent system with negotiation agents for e-trading products and services
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
Learning opponent's preferences for effective negotiation: an approach based on concept learning
Autonomous Agents and Multi-Agent Systems
The learning of an opponent's approximate preferences in bilateral automated negotiation
Journal of Theoretical and Applied Electronic Commerce Research
A time-constrained SLA negotiation strategy in competitive computational grids
Future Generation Computer Systems
Using Gaussian processes to optimise concession in complex negotiations against unknown opponents
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
A multi-choice offer strategy for bilateral multi-issue negotiations using modified DWM learning
Proceedings of the 13th International Conference on Electronic Commerce
Cloning mechanisms to improve agent performances
Journal of Network and Computer Applications
Measuring the performance of online opponent models in automated bilateral negotiation
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
ABiNeS: An Adaptive Bilateral Negotiating Strategy over Multiple Items
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Optimizing complex automated negotiation using sparse pseudo-input gaussian processes
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
An agent design for repeated negotiation and information revelation with people: Extended Abstract
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Tasks for agent-based negotiation teams: Analysis, review, and challenges
Engineering Applications of Artificial Intelligence
Strategies for avoiding preference profiling in agent-based e-commerce environments
Applied Intelligence
Expectation of trading agent behaviour in negotiation of electronic marketplace
Web Intelligence and Agent Systems
Effective acceptance conditions in real-time automated negotiation
Decision Support Systems
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The efficiency of automated multi-issue negotiation depends on the availability and quality of knowledge about an opponent. We present a generic framework based on Bayesian learning to learn an opponent model, i.e. the issue preferences as well as the issue priorities of an opponent. The algorithm proposed is able to effectively learn opponent preferences from bid exchanges by making some assumptions about the preference structure and rationality of the bidding process. The assumptions used are general and consist among others of assumptions about the independency of issue preferences and the topology of functions that are used to model such preferences. Additionally, a rationality assumption is introduced that assumes that agents use a concession-based strategy. It thus extends and generalizes previous work on learning in negotiation by introducing a technique to learn an opponent model for multi-issue negotiations. We present experimental results demonstrating the effectiveness of our approach and discuss an approximation algorithm to ensure scalability of the learning algorithm.