The weighted majority algorithm
Information and Computation
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Multiple Negotiations among Agents for a Distributed Meeting Scheduler
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Learning Dynamic Preferences in Multi-Agent Meeting Scheduling
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Learning to select negotiation strategies in multi-agent meeting scheduling
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Multiagent and Grid Systems - Advances in Agent-mediated Automated Negotiations
Agent and multi-agent applications to support distributed communities of practice: a short review
Autonomous Agents and Multi-Agent Systems
A personal meeting scheduling agent
Personal and Ubiquitous Computing
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In the multiagent meeting scheduling problem, agents negotiate with each other on behalf of their users to schedule meetings. While a number of negotiation approaches have been proposed for scheduling meetings, it is not well understood how agents can negotiate strategically in order to maximize their users' utility. To negotiate strategically, agents need to learn to pick good strategies for negotiating with other agents. In this paper, we show how agents can learn online to negotiate strategically in order to better satisfy their users' preferences. We outline the applicability of experts algorithms to the problem of learning to select negotiation strategies. In particular, we show how two different experts approaches, plays [3] and Exploration---Exploitation Experts (EEE) [10] can be adapted to the task. We show experimentally the effectiveness of our approach for learning to negotiate strategically.