IJCAI 2020poster0 citations

On the Learnability of Possibilistic Theories

Cosimo Persia, Ana Ozaki

Abstract

We investigate learnability of possibilistic theories from entailments in light of Angluin’s exact learning model. We consider cases in which only membership, only equivalence, and both kinds of queries can be posed by the learner. We then show that, for a large class of problems, polynomial time learnability results for classical logic can be transferred to the respective possibilistic extension. In particular, it follows from our results that the possibilistic extension of propositional Horn theories is exactly learnable in polynomial time. As polynomial time learnability in the exact model is transferable to the classical probably approximately correct (PAC) model extended with membership queries, our work also establishes such results in this model.

Knowledge Representation and Reasoning: Logics for Knowledge RepresentationMachine Learning: Learning TheoryUncertainty in AI: Uncertainty Representations
BibTeX
@inproceedings{ijcai2020p259,
  title     = {On the Learnability of Possibilistic Theories},
  author    = {Persia, Cosimo and Ozaki, Ana},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Christian Bessiere},
  pages     = {1870--1876},
  year      = {2020},
  month     = {7},
  note      = {Main track},
  doi       = {10.24963/ijcai.2020/259},
  url       = {https://doi.org/10.24963/ijcai.2020/259},
}