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Melhoria na converg?ncia do algoritmo Q-Learning na aplica??o de sistemas tutores inteligentesPaiva, ?verton de Oliveira 16 August 2016 (has links)
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Previous issue date: 2016 / O uso sistemas computacionais como complemento ou substitui??o da sala de aula ? cada vez
mais comum na educa??o e os Sistemas Tutores Inteligentes (STIs) s?o uma dessas alternativas.
Portanto ? fundamental desenvolver STIs capazes tanto de ensinar quanto aprender informa??es
relevantes sobre o aluno atrav?s de t?cnicas de intelig?ncia artificial. Esse aprendizado acontece
por meio da intera??o direta entre o STI e o aluno que ? geralmente demorada. Esta disserta??o
apresenta a inser??o da metaheur?sticas Lista Tabu e GRASP com o objetivo de acelerar esse
aprendizado. Para avaliar o desempenho dessa modifica??o, foi desenvolvido um simulador de
STI. Nesse sistema, foram realizadas simula??es computacionais para comparar o desempenho
da tradicional pol?tica de explora??o aleat?ria e as metaheur?sticas propostas Lista Tabu e
GRASP. Os resultados obtidos atrav?s dessas simula??es e os testes estat?sticos aplicados
indicam fortemente que a introdu??o de meta-heur?sticas adequadas melhoram o desempenho
do algoritmo de aprendizado em STIs. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2016. / 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 dissertation presents the insertion of meta-heuristic Tabu search and GRASP
with the purpose of accelera ting learning. An ITS simulator was developed to evaluate the
performance of this change. 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 and statistical tests strongly indicate that the
introduction of meta-heuristics in exploration policy improves the performance of the learning
algorithm in ITS.
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