An experts approach to strategy selection in multiagent meeting scheduling |
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Authors: | Elisabeth Crawford Manuela Veloso |
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Affiliation: | (1) Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA |
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Abstract: | 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. |
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Keywords: | Multiagent meeting scheduling Negotiation Multiagent learning Experts algorithms Applications |
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