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].
A Knowledge Level Account of Forgetting
Authors: James P. Delgrande
JAIR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is an attempt to address these issues. Forgetting is considered here as an abstract belief change operator, independent of any specific formal system. That is, the goal is to investigate forgetting at the knowledge level (Newell, 1981) and thus independent of syntax. ... Since the approach is expressed at the knowledge level, it is foremost representational, providing a characterisation of forgetting via a definition and desired properties, in a logicindependent fashion. Consequently, issues of implementation and inference are not the focus of the paper. |
| Researcher Affiliation | Academia | James P. Delgrande EMAIL School of Computing Science Simon Fraser University Burnaby, B.C. V5A 1S6 Canada |
| Pseudocode | No | The paper primarily discusses theoretical concepts, definitions, and theorems in logical frameworks. It does not contain explicit pseudocode or algorithm blocks in the traditional sense of describing computational procedures. |
| Open Source Code | No | The paper does not contain any explicit statements regarding the release of source code, nor does it provide links to any code repositories or mention code in supplementary materials. |
| Open Datasets | No | The paper is theoretical and focuses on logical formalisms; it does not use or describe any datasets, publicly available or otherwise, for experimental purposes. |
| Dataset Splits | No | Since this paper is theoretical and does not involve experiments with datasets, there is no information provided regarding dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental work that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper discusses various logical systems but does not provide specific software dependencies (e.g., library names with version numbers) that would be needed to replicate experimental results, as it is a theoretical work. |
| Experiment Setup | No | The paper is theoretical and focuses on abstract definitions and properties of forgetting in logic. It does not describe any experiments or their setup, including hyperparameters or system-level training settings. |