Open AccessDoes Complex Learning Require Complex Connectivity?
Author(s) -
Carlos Rubén de la Mora-Basáñez,
Alejandro GuerraHernández,
Luc Steels
Publication year - 2006
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
ISBN - 3-540-45462-4
DOI - 10.1007/11874850_61
Subject(s) - computer science , complex network , situated , artificial intelligence , context (archaeology) , complex system , small world network , autonomy , scale (ratio) , cognition , theoretical computer science , cognitive science , world wide web , paleontology , physics , quantum mechanics , neuroscience , political science , law , biology , psychology
Trabajo presentado a la 2nd International Joint Conference, 10th Ibero-American Conference on AI and 18th Brazilian AI Symposium; celebrados en Ribeirão Preto (Brasil) del 23 al 27 de octubre de 2006.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.Peer reviewe
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