Name
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s10458-006-0010-2 | Autonomous Agents and Multi-Agent Systems |
Keywords | Field | DocType |
Multiagent meeting scheduling,Negotiation,Multiagent learning,Experts algorithms,Applications | Job shop scheduling,Computer science,Scheduling (computing),Multiagent learning,Management science,Negotiation | Journal |
Volume | Issue | ISSN |
15 | 1 | 1387-2532 |
Citations | PageRank | References |
6 | 0.49 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elisabeth Crawford | 1 | 63 | 6.26 |
| Manuela Veloso | 2 | 8563 | 882.50 |