Improving Example Selection for Agents Teaching Ontology Concepts

  • Mohsen Afsharchi
  • Behrouz H. Far
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4149)


We present a method to improve the positive examples selection by teaching agents in a multi-agent system in which a team of agent peers teach concepts to a learning agent. The basic idea in this method is to let a teacher agent expand the features it uses to describe a concept in its ontology by additional features. This resembles the typical behavior of human teachers who describe concepts from different viewpoints in the hope that one of these viewpoints comes close to the viewpoint of a learner. The extended feature set is then used to select positive examples that together with negative examples are communicated to the learner agent. The learner uses concept learning techniques to integrate the new concept into its own ontology. An experimental evaluation shows a significant learning improvement compared to the previous approach.


Feature Selection Core Feature Discriminative Feature Formal Concept Analysis Interaction Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mohsen Afsharchi
    • 1
  • Behrouz H. Far
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of CalgaryCalgaryCanada

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