Title :
Multi-agent memetic computing for adaptive learning experiences
Author :
Acampora, Giovanni ; Gaeta, Matteo ; Loia, Vincenzo ; Vitiello, Autilia
Author_Institution :
Dept. of Math. & Comput. Sci., Univ. of Salerno, Salerno, Italy
Abstract :
Learning is a mechanism to acquire new knowledge and to enhance individual skills in industrial and academic environments. In particular, employing learning methods in an industrial context supports the overall business competitiveness in the new economy. Currently, the e-Learning systems provide a simple “digitalization” of the learning process where the focus is on the educational resources, which are only an input of the whole learning process, and on their presentation (delivery). Computational Intelligence methodologies can overcome current learning systems limitations attaining to personalize learning content and activities to specific preferences of the learner and to assist designers with computationally efficient methods to develop “in time” e-Learning environments. This paper shows how to achieve both results exploiting an ontological representation of learning environment and memetic approach of optimization, integrated into a cooperative distributed problem solving framework.
Keywords :
computer aided instruction; evolutionary computation; multi-agent systems; ontologies (artificial intelligence); adaptive learning experiences; computational intelligence; cooperative distributed problem solving framework; e-learning systems; educational resources; multi-agent memetic computing; ontological representation; optimization; Computational modeling; Electronic learning; Genetics; Memetics; Optimization; Program processors;
Conference_Titel :
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6919-2
DOI :
10.1109/FUZZY.2010.5584436