A probabilistic model of affective behavior for intelligent tutoring systems
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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Training of operators has become an important problem to be faced by power systems: updating knowledge and skills. An operator must comprehend the physical operation of the process and must be skilled in handling a number of normal and abnormal operating problems and emergencies. We are developing an intelligent environment for training of power system operators. This paper presents the architecture of the intelligent environment composed by reusable learning objects, concept structure maps, operator cognitive and affective model, tutor and adaptive sequence, and learning interface. The operator model and adaptive sequence are represented by probabilistic networks that select the best pedagogical and affective action for each specific operator. The model was evaluated using scholar environments with good results. The general aim of our work is to provide operators of complex industrial environments with a suitable training from a pedagogical and affective viewpoint to certify operators in knowledge.