Intelligent Tutoring Systems by Bayesian Nets with Noisy Gates
A. Antonucci,Francesca Mangili,Claudio Bonesana,Giorgia Adorni +3 more
- Vol. 35
TLDR
This work advocates logical gates with uncertainty for a compact parametrization of the conditional probability tables in the underlying Bayesian net used by tutoring systems and derives a dedicated inference scheme to speed up computations.Abstract:
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.read more
References
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Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Book
Probabilistic graphical models : principles and techniques
Daniel L. Koller,Nir Friedman +1 more
TL;DR: The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.
Book ChapterDOI
Adaptive Bayesian Networks for Multilevel Student Modelling
TL;DR: An integrated theoretical approach for student modelling based on an Adaptive Bayesian Network is provided, and new question selection criteria presented, and a tool to assist in the diagnosis process has been implemented.
Bayesian Student Modelling and Decision-Theoretic Selection of Tutorial Actions in Intelligent Tutoring Systems
TL;DR: It is argued that Bayesian nets can offer much more to an ITS, and an example of how they can be used for selecting problems is given, to automating many kinds of decision in ITSs.
Proceedings Article
The imprecise noisy-OR gate
TL;DR: It is possible to prove that, exactly as for Bayesian networks, the local complexity to update probabilities on an imprecise noisy-OR gate takes only linear, instead of exponential, time in the number of causes.