Machine Learning
Games That Agents Play: A Formal Framework for Dialogues between Autonomous Agents
Journal of Logic, Language and Information
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Argument based machine learning
Artificial Intelligence
Collaborative plans for group activities
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Artificial Intelligence
Resource use pattern analysis for predicting resource availability in opportunistic grids
Concurrency and Computation: Practice & Experience - Advanced Scheduling Strategies and Grid Programming Environments
Agent Support for Policy-Driven Collaborative Mission Planning
The Computer Journal
Argumentation strategies for plan resourcing
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
On the benefits of argumentation-derived evidence in learning policies
ArgMAS'10 Proceedings of the 7th international conference on Argumentation in Multi-Agent Systems
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An important question for an autonomous agent deciding whom to approach for a resource or for an action to be done is what do I need to say to convince you to do something? 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 refined 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 significantly improve the cumulative utility of dialogical outcomes, and help to reduce communication overhead.