An Incremental Model of Lexicon Consensus in a Population of Agents by Means of Grammatical Evolution, Reinforcement Learning and Semantic Rules

  • Jack Mario Mingo
  • Ricardo Aler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)


We present an incremental model of lexicon consensus in a population of simulated agents. The emergent lexicon is evolved with a hybrid algorithm which is based on grammatical evolution with semantic rules and reinforcement learning. The incremental model allows to add subsequently new agents and objects to the environment when a consensual language has emerged for a steady set of agents and objects. The main goal in the proposed system is to test whether the emergent lexicon can be maintained during the execution when new agents and object are added. The proposed system is completely based on grammars and the results achieved in the experiments show how building a language starting from a grammar can be a promising method in order to develop artificial languages.


Swarm Intelligence Grammatical Evolution Language Acquisition and Language Development 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jack Mario Mingo
    • 1
  • Ricardo Aler
    • 2
  1. 1.Computer Science DepartmentAutonomous University of MadridSpain
  2. 2.Computer Science DepartmentCarlos III University of MadridSpain

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