Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Applied Cryptography: Protocols, Algorithms, and Source Code in C
Applied Cryptography: Protocols, Algorithms, and Source Code in C
Machine Learning
Games That Agents Play: A Formal Framework for Dialogues between Autonomous Agents
Journal of Logic, Language and Information
Learning silhouette features for control of human motion
ACM Transactions on Graphics (TOG)
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
Agent Support for Policy-Driven Collaborative Mission Planning
The Computer Journal
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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What argument(s) do I put forward in order to persuade another agent to do something for me? This is an important question for an autonomous agent collaborating with others to solve a problem. How effective were similar arguments in convincing similar agents in similar circumstances? What are the risks associated with putting certain arguments forward? Can agents exploit evidence derived from past dialogues to improve the outcome of delegation decisions? In this paper, we present an agent decision-making mechanism where models of other agents are refined through evidence derived from dialogues, and where these models are used to guide future argumentation strategy. We combine argumentation, machine learning and decision theory in a novel way that enables agents to reason about constraints (e.g., policies) that others are operating within, and make informed decisions about whom to delegate a task to. We demonstrate the utility of this novel approach through empirical evaluation in a plan resourcing domain. Our evaluation shows that a combination of decision-theoretic and machine learning techniques can significantly help to improve dialogical outcomes.