KQML as an agent communication language
Software agents
Machine Learning
The Origins of Ontologies and Communication Conventions in Multi-Agent Systems
Autonomous Agents and Multi-Agent Systems
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning to Share Meaning in a Multi-Agent System
Autonomous Agents and Multi-Agent Systems
Learning Non-Unanimous Ontology Concepts to Communicate with Groups of Agents
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
A cooperation-based approach for evolution of service ontologies
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Evolving service semantics cooperatively: a consumer-driven approach
Autonomous Agents and Multi-Agent Systems
Enhancing communication with groups of agents using learned non-unanimous ontology concepts
Web Intelligence and Agent Systems
Incremental Non-unanimous Concept Reformation through Queried Object Classification
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Forgetting fragments from evolving ontologies
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Improving example selection for agents teaching ontology concepts
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
An on-line algorithm for semantic forgetting
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Join Me with the Weakest Partner, Please
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Information Systems Frontiers
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We present a general method for agents using ontologies as part of their knowledge representation to teach each other concepts to improve their communication and thus cooperation abilities. Our method aims at getting positive and negative examples for a concept only very vaguely understood by a particular agent from the other agents. This agent then uses one of the known concept learning methods to learn the concept in question, involving the other agents again by taking votes in case of conflicts in the received knowledge. This method allows agents that are not sharing common ontologies to establish common grounds on concepts known only to some of them, if these common grounds are needed during cooperation. While the concepts learned by an agent are only compromises between the views of the other agents, the method nevertheless enhances the autonomy of agents using it substantially.