Instance-Based Learning Algorithms
Machine Learning
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Machine Learning
Incremental Learning with Partial Instance Memory
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Multi-Issue Negotiation Processes by Evolutionary Simulation, Validationand Social Extensions
Computational Economics
An agenda-based framework for multi-issue negotiation
Artificial Intelligence
Learning on opponent's preferences to make effective multi-issue negotiation trade-offs
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Modeling opponent decision in repeated one-shot negotiations
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Adaptive negotiation agents for e-business
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
Designing intelligent sales-agent for online selling
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
Learning opponents' preferences in multi-object automated negotiation
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning negotiation policies using IB3 and Bayesian networks
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Detecting drifts in multi-issue negotiations
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Hi-index | 0.00 |
In this work, we propose the use of drift detection techniques for learning offer policies in multiissue, bilateral negotiation. Several works aiming to develop adaptive trading agents have been proposed. Such agents are capable of learning their competitors' utility values and functions, thereby obtaining better results in negotiation. However, the learning mechanisms generally used disregard possible changes in a competitor's offer/counter-offer policy. In that case, the agent's performance may decrease drastically. The agent then needs to restart the learning process, as the model previously learned is no longer valid. Drift detection techniques can be used to detect changes in the current offers model and quickly update it. In this work, we demonstrate with simulated data that drift detection algorithms can be used to build adaptive trading agents and offer a number of advantages over the techniques mostly used in this problem. The results obtained with the algorithm IB3 (instance-based) show that the agent's performance can be rapidly recovered even when changes interesting to the competitor are abrupt, moderate, or gradual.