Making Rational Decisions Using Adaptive Utility Elicitation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Preference elicitation via theory refinement
The Journal of Machine Learning Research
An agenda-based framework for multi-issue negotiation
Artificial Intelligence
Optimal Negotiation of Multiple Issues in Incomplete Information Settings
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Learning on opponent's preferences to make effective multi-issue negotiation trade-offs
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Detecting opponent concessions in multi-issue automated negotiation
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
LEARNING DRIFTING NEGOTIATIONS
Applied Artificial Intelligence
Using temporal-difference learning for multi-agent bargaining
Electronic Commerce Research and Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Applying hybrid case-based reasoning in agent-based negotiations for supply chain management
Expert Systems with Applications: An International Journal
Learning opponent's preferences for effective negotiation: an approach based on concept learning
Autonomous Agents and Multi-Agent Systems
A social approach for learning agents
Expert Systems with Applications: An International Journal
Strategies for avoiding preference profiling in agent-based e-commerce environments
Applied Intelligence
| Hi-index | 0.00 |
We present a classification method for learning an opponent's preferences during a bilateral multi-issue negotiation. Similar candidate preference relations are grouped into classes, and a Bayesian technique is used to determine, for each class, the likelihood that the opponent's true preference relation over the set of offers lies in that class. Evidence used for classification decision-making is obtained by observing the opponents' sequence of offers, and applying the concession assumption, which states that negotiators usually decrease their offer utilities as time passes in order to find a deal. Simple experiments show that the technique can find the correct class after very few offers and can select a preference relation that is likely to match closely with the opponent's true preferences.