The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
Extending the recognition-primed decision model to support human-agent collaboration
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A theoretical framework on proactive information exchange in agent teamwork
Artificial Intelligence
Agents with shared mental models for enhancing team decision makings
Decision Support Systems - Special issue: Intelligence and security informatics
RPD-enabled agents teaming with humans for multi-context decision making
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Boosting-Based Distributed and Adaptive Security-Monitoring through Agent Collaboration
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
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The capability of learning from experience is of critical importance in developing multi-agent systems supporting dynamic group decision making. In this paper, we introduce a hierarchical learning approach, aiming to support hierarchical group decision making where the decision makers at lower levels only have partial view of the whole picture. To further understand such a hierarchical learning concept, we implemented a learning component within the R-CAST agent architecture, with lower-level learners using the LogitBoost algorithm with decision stumps. The boosting-based learning agents were then used in our experiments to classify experience instances. The results indicate that hierarchical learning can largely improve decision accuracy when lower-level decision makers only have limited information accessibility.