Does complex learning require complex connectivity?

  • Authors:
  • Carlos Rubén de la Mora-Basáñez;Alejandro Guerra-Hernández;Luc Steels

  • Affiliations:
  • Departamento de Inteligencia Artificial, Universidad Veracruzana, Facultad de Física e Inteligencia Artificial, Xalapa, Ver., México;Departamento de Inteligencia Artificial, Universidad Veracruzana, Facultad de Física e Inteligencia Artificial, Xalapa, Ver., México;Artificial Intelligence Laboratory, Vrije Universiteit Brussel, Brussels, Belgium

  • Venue:
  • IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Small World and Scale Free network properties characterize many real complex phenomena. We assume that low level connectivity with such topological properties, e.g., anatomical or functional connectivity in brains, is compulsory to achieve high level cognitive functionality, as language. The study of these network properties provides tools to approach different issues in behavior based Artificial Intelligence (AI) that usually have been ill defined, e.g., complexity and autonomy. In this paper, we propose a model in which situated agents evolve knowledge networks holding both Small World and Scale Free properties. Experimental results in the context of Pragmatic Games, elucidate some required conditions to obtain the expected network properties when performing complex learning.