Games That Agents Play: A Formal Framework for Dialogues between Autonomous Agents
Journal of Logic, Language and Information
ATAL '00 Proceedings of the 7th International Workshop on Intelligent Agents VII. Agent Theories Architectures and Languages
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Agent Mining: The Synergy of Agents and Data Mining
IEEE Intelligent Systems
Foundations of Semantic Web Technologies
Foundations of Semantic Web Technologies
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
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In multi-agent systems, agents often depend on others to act on their behalf. However, delegation decisions are complicated in norm-governed environments, where agents' activities are regulated by policies. Especially when such policies are not public, learning these policies become critical to estimate the outcome of delegation decisions. In this paper, we propose the use of domain knowledge in aiding the learning of policies. Our approach combines ontological reasoning, machine learning and argumentation in a novel way for identifying, learning, and modeling policies. Using our approach, software agents can autonomously reason about the policies that others are operating with, and make informed decisions about to whom to delegate a task. In a set of experiments, we demonstrate the utility of this novel combination of techniques through empirical evaluation. Our evaluation shows that more accurate models of others' policies can be developed more rapidly using various forms of domain knowledge.