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Antonucci_2021_flairs
Conference paper (in proceedings)

Intelligent tutoring systems by Bayesian nets with noisy gates

  • Antonucci, Alessandro ORCID Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
  • Mangili, Francesca ORCID Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
  • Bonesana, Claudio ORCID Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
  • Adorni, Giorgia ORCID Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
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  • 2021
Published in:
  • Proceedings of FLAIRS-35. - 2021, vol. 35, no. 2022
Bayesian networks
Intelligent tutoring systems
Noisy-OR gates
English 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.
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Language
  • English
Classification
Computer science and technology
License
CC BY-NC
Open access status
gold
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Persistent URL
https://n2t.net/ark:/12658/srd1326771
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