Intelligent tutoring systems by Bayesian nets with noisy gates
This work addresses the challenge of making Bayesian networks more practical for real-time intelligent tutoring systems by reducing parameter elicitation burden and improving inference speed.
The paper tackles the problem of reducing parameter complexity in Bayesian network-based intelligent tutoring systems by proposing logical gates with uncertainty for compact conditional probability table parametrization, and develops a dedicated inference scheme to accelerate computations.
Directed graphical models such as Bayesian nets are often used to implement intelligent tutoring systems able to interact in real-time with learners in a purely automatic way. When coping with such models, keeping a bound on the number of parameters might be important for multiple reasons. First, as these models are typically based on expert knowledge, a huge number of parameters to elicit might discourage practitioners from adopting them. Moreover, the number of model parameters affects the complexity of the inferences, while a fast computation of the queries is needed for real-time feedback. We advocate logical gates with uncertainty for a compact parametrization of the conditional probability tables in the underlying Bayesian net used by tutoring systems. We discuss the semantics of the model parameters to elicit and the assumptions required to apply such approach in this domain. We also derive a dedicated inference scheme to speed up computations.