Abstract
What do I need to say to convince you to do something? This is an important question for an autonomous agent deciding whom to approach for a resource or for an action to be done. Were similar requests granted from similar agents in similar circumstances? What arguments were most persuasive? What are the costs involved in putting certain arguments forward? In this paper we present an agent decision-making mechanism where models of other agents are rened through evidence from past dialogues, and where these models are used to guide future argumentation strategy. We empirically evaluate our approach to demonstrate that decision-theoretic and machine learning techniques can both signicantly improve the cumulative utility of dialogical outcomes, and help to reduce communication overhead.
Original language | English |
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Title of host publication | Proceedings of the Tenth International Conference on Autonomous Agents and Multiagent Systems |
Place of Publication | Richland, SC |
Publisher | IFAAMAS |
Pages | 913-920 |
Number of pages | 8 |
Volume | 3 |
ISBN (Print) | 0-9826571-7-X, 978-0-9826571-7-1 |
Publication status | Published - 2011 |
Event | AAMAS'11 The Tenth International Conference on Autonomous Agents and Multiagent Systems - Taipei, Taiwan, Province of China Duration: 2 May 2011 → 6 May 2011 |
Conference
Conference | AAMAS'11 The Tenth International Conference on Autonomous Agents and Multiagent Systems |
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Country/Territory | Taiwan, Province of China |
City | Taipei |
Period | 2/05/11 → 6/05/11 |