An incremental model of lexicon consensus in a population of agents by means of grammatical evolution, reinforcement learning and semantic rules

  • Authors:
  • Jack Mario Mingo;Ricardo Aler

  • Affiliations:
  • Computer Science Department, Autonomous University of Madrid;Computer Science Department, Carlos III University of Madrid

  • Venue:
  • IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.