Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
On the Learnability of Possibilistic Theories
Authors: Cosimo Persia, Ana Ozaki
IJCAI 2020 | Venue PDF | 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. |