Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
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When a tutoring aims to guide students in the teaching/learning process, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. The Bayesian framework offers a number of techniques for inferring individual's knowledge state from evidence of mastery of concepts or skills. Using Bayesian networks, we have devised the probabilistic student models for MacBay, a tutoring system that is an authoring tool. MacBay's models provide prediction of student's action during teaching/learning process. We combined the Concept Maps and the Bayesian networks in order to obtain a Concept Map with intelligent behavior, where "the intelligence" is considered as the capacity to adapt the interaction to its user's specific needs. In this paper we describe the way in that we do this combination and inference process.