Concept Convergence in Empirical Domains

  • Santiago Ontañón
  • Enric Plaza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6332)


How to achieve shared meaning is a significant issue when more than one intelligent agent is involved in the same domain. We define the task of concept convergence, by which intelligent agents can achieve a shared, agreed-upon meaning of a concept (restricted to empirical domains). For this purpose we present a framework that, integrating computational argumentation and inductive concept learning, allows a pair of agents to (1) learn a concept in an empirical domain, (2) argue about the concept’s meaning, and (3) reach a shared agreed-upon concept definition. We apply this framework to marine sponges, a biological domain where the actual definitions of concepts such as orders, families and species are currently open to discussion. An experimental evaluation on marine sponges shows that concept convergence is achieved, within a reasonable number of interchanged arguments, and reaching short and accurate definitions (with respect to precision and recall).


Multiagent System Marine Sponge Belief Revision Argumentation Framework Inductive Learning 
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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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Santiago Ontañón
    • 1
  • Enric Plaza
    • 1
  1. 1.IIIA, Artificial Intelligence Research Institute, CSIC, Spanish Council for Scientific Research, Campus UABBellaterraSpain

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