Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Collaborative multiagent learning for classification tasks
Proceedings of the fifth international conference on Autonomous agents
Mutual online concept learning for multiple agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Machine Learning
A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Incremental learning with partial instance memory
Artificial Intelligence
Lookahead-based algorithms for anytime induction of decision trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Towards tight bounds for rule learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Recycling data for multi-agent learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning in BDI multi-agent systems
CLIMA IV'04 Proceedings of the 4th international conference on Computational Logic in Multi-Agent Systems
When agents communicate hypotheses in critical situations
DALT'06 Proceedings of the 4th international conference on Declarative Agent Languages and Technologies
A Framework for Knowledge Discovery in a Society of Agents
DS '08 Proceedings of the 11th International Conference on Discovery Science
Multiagent Incremental Learning in Networks
PRIMA '08 Proceedings of the 11th Pacific Rim International Conference on Multi-Agents: Intelligent Agents and Multi-Agent Systems
Learning in a Fixed or Evolving Network of Agents
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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This article deals with the problem of collaborative learning in a multi-agent system. Here each agent can update incrementally its beliefs B (the concept representation) so that it is in a way kept consistent with the whole set of information K (the examples) that he has received from the environment or other agents. We extend this notion of consistency (or soundness) to the whole MAS and discuss how to obtain that, at any moment, a same consistent concept representation is present in each agent. The corresponding protocol is applied to supervised concept learning. The resulting method SMILE (standing for Sound Multi-agent Incremental LEarning) is described and experimented here. Surprisingly some difficult boolean formulas are better learned, given the same learning set, by a Multi agent system than by a single agent.