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..
Learning Logic Programs Though Divide, Constrain, and Conquer
Authors: Andrew Cropper6446-6453
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on three domains (classification, inductive general game playing, and program synthesis) show that our approach can increase predictive accuracies and reduce learning times. 5 Experiments We claim that DCC can reduce search complexity and thus improve learning performance. To evaluate this claim, our experiments aim to answer the question: Q1 Can DCC improve predictive accuracies and reduce learning times? |
| Researcher Affiliation | Academia | Andrew Cropper University of Oxford EMAIL |
| Pseudocode | Yes | Algorithm 1 shows the POPPER algorithm, which solves the LFF problem (Definition 1). Algorithm 2 shows the DCC algorithm. |
| Open Source Code | Yes | The experimental code and data are available at https://github.com/logic-and-learning-lab/aaai22-dcc. |
| Open Datasets | Yes | Michalski Trains (Larson and Michalski 1977) is a classical problem. In inductive general game playing (IGGP) (Cropper, Evans, and Law 2020) agents are given game traces... We use the program synthesis dataset introduced by Cropper and Morel (2021a). |
| Dataset Splits | No | We randomly sample the examples and split them into 80/20 train/test partitions. |
| Hardware Specification | Yes | We use a 3.8 GHz 8-Core Intel Core i7 with 32GB of ram. |
| Software Dependencies | No | The paper mentions 'Clingo (Gebser et al. 2014), an ASP system' as a component used, but does not provide specific version numbers for software dependencies needed to reproduce the experiments, nor a list of multiple key software components with versions. |
| Experiment Setup | Yes | We enforce a timeout of five minutes per task. We repeat all the experiments 20 times and measure the mean and standard deviation. We use a 3.8 GHz 8-Core Intel Core i7 with 32GB of ram. All the systems use a single CPU. |