An experts approach to strategy selection in multiagent meeting scheduling

Author:

Crawford Elisabeth,Veloso Manuela

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference17 articles.

1. Auer, P., Cesa-Bianchi, N., Freund, Y., & Schapire, R. (1995).Gambling in a rigged casino: the adversarial multi-armed bandit problem. In: Proceedings of the 36th annual FOCS.

2. Bowling, M. (2005). Convergence and no-regret in multiagent learning. (pp. 209–216) In: Advances in neural information processing systems17

3. Bowling, M., Browning, B., & Veloso, M. (2004). Plays as elective multiagent plans enabling opponent-adaptive play selection. In: Proceedings of International conference on automated planning and scheduling (ICAPS’04).

4. Crawford, E., & Veloso, M. (2004) Opportunities for learning in multi-agent meeting scheduling. In: Proceedings of the AAAI symposium on artificial multiagent learning.

5. Crawford, E., & Veloso, M. (2005). Learning to select negotiation strategies in multi-agent meeting scheduling. In: Proceedings of 12th portugese conference on artificial intelligence. Springer, LNCS.

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1. A personal meeting scheduling agent;Personal and Ubiquitous Computing;2013-07-04

2. A Deliberative Agent for Meeting Scheduling;2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA);2013-03

3. A Negotiation Protocol for Meeting Scheduling Agent;Procedia Computer Science;2013

4. Selecting Negotiation Strategies for Meeting Scheduling Using a Model Based Approach;Procedia Computer Science;2012

5. Agent and multi-agent applications to support distributed communities of practice: a short review;Autonomous Agents and Multi-Agent Systems;2011-04-05

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