On the Learnability of Possibilistic Theories

Authors: Cosimo Persia, Ana Ozaki

IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical 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 model extended with membership queries, our work also establishes such results in this model.
Researcher Affiliation Academia Cosimo Persia and Ana Ozaki University of Bergen
Pseudocode No The paper describes procedures like
Open Source Code No The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available.
Open Datasets No This is a theoretical paper and does not involve the use of datasets for training.
Dataset Splits No This is a theoretical paper and does not involve the use of data for training, validation, or testing, thus no dataset splits are mentioned.
Hardware Specification No This is a theoretical paper and does not describe any experiments that would require hardware specifications.
Software Dependencies No This is a theoretical paper and does not describe any software dependencies with specific version numbers relevant for empirical experiments.
Experiment Setup No This is a theoretical paper and does not include details about an experimental setup, hyperparameters, or training configurations.