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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Forgetting to Learn Logic Programs
Authors: Andrew Cropper3676-3683
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that Forgetgol outperforms the alternative approaches when learning from over 10,000 tasks. |
| Researcher Affiliation | Academia | Andrew Cropper University of Oxford EMAIL |
| Pseudocode | Yes | Algorithm 1 Forgetgol |
| Open Source Code | Yes | All the experimental data are available at https://github.com/andrewcropper/aaai20-forgetgol. |
| Open Datasets | Yes | All the experimental data are available at https://github.com/andrewcropper/aaai20-forgetgol. |
| Dataset Splits | No | The paper uses a multi-task learning setup where tasks are generated. It measures performance on the percentage of tasks solved and learning times, but does not specify traditional train/validation/test splits for each task's underlying examples, nor does it specify a validation split for the overall set of tasks. |
| Hardware Specification | No | The paper does not specify the hardware used for the experiments. |
| Software Dependencies | No | The paper mentions Metagol, a MIL system based on a Prolog meta-interpreter, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We enforce a timeout of 60 seconds per task per search depth. We set the maximum program size to 6 clauses. |