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Vol.3 , No. 1, Publication Date: May 13, 2016, Page: 1-5
 are one of these alternatives. Therefore, it is crucial to develop ITS that are capable of both teaching and learning relevant information about the student through artificial intelligence techniques. This learning process occurs by means of direct, and generally slow, interaction between the ITS and the student. This article presents the insertion of meta-heuristic Tabu search with the purpose of accelerating learning. Computer simulations were conducted in order to compare the performance of traditional randomized search methods with the meta-heuristic Tabu search. Results obtained from these simulations strongly indicate that the introduction of meta-heuristics in exploration policy improves the performance of the learning algorithm in STI.&picture=http://www.aascit.org/aascit/decorators/img/journal/910.jpg)
| [1] | Éverton de Oliveira Paiva, Management of Educational Institutions - GIED, Federal University of Jequitinhonha and Mucuri Valleys – UFVJM, Diamantina, Brazil. |
| [2] | Marcus Vinicius Carvalho Guelpeli, Department of Computing – DECOM, Federal University of Jequitinhonha and Mucuri Valleys – UFVJM, Diamantina, Brazil. |
Using computer systems as a complement or replacement for the classroom experience is an increasingly common practice in education, and Intelligent Tutoring Systems (ITS) are one of these alternatives. Therefore, it is crucial to develop ITS that are capable of both teaching and learning relevant information about the student through artificial intelligence techniques. This learning process occurs by means of direct, and generally slow, interaction between the ITS and the student. This article presents the insertion of meta-heuristic Tabu search with the purpose of accelerating learning. Computer simulations were conducted in order to compare the performance of traditional randomized search methods with the meta-heuristic Tabu search. Results obtained from these simulations strongly indicate that the introduction of meta-heuristics in exploration policy improves the performance of the learning algorithm in STI.
Keywords
Intelligent Tutoring System, Autonomous Model, Q-Learning, Convergence Improvement, Tabu Search
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