Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning
Authors: Céline Hocquette, Stephen H. Muggleton
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments 6.1 Research Hypotheses We experimentally test within this section whether the use of bottom-up iterations can improve learning performance. [...] Results. Accuracy results are presented in Figure 3a: a T-test suggests that the difference in accuracy is statistically significant (p < 0.05) for a training set up with up to 16 instances thus refuting Null Hypothesis 1. Sample complexity results are detailed in Figure 3c and show that performing bottom-up iterations can reduce the sample complexity. |
| Researcher Affiliation | Academia | Celine Hocquette, Stephen H. Muggleton Department of Computing, Imperial College London, London, UK {celine.hocquette16, s.muggleton}@imperial.ac.uk |
| Pseudocode | Yes | Algorithm 1 Bottom-Up Learner Input: second-order logic program B|E related to training examples E, number of iterations k, definitions of initial predicate H0 Output: logic program H |
| Open Source Code | Yes | The code for reproducing the experiments is available at https://github.com/celinehocquette/bottom_up.git |
| Open Datasets | Yes | We consider 94 real-world string transformation problems evaluated in [Cropper, 2019] and inspired from [Lin et al., 2014; Gulwani, 2011]. The dataset contains 10 positive examples for each problem. |
| Dataset Splits | No | The paper describes training and testing sets, and the split between them, but does not explicitly mention the use of a separate validation set or its split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions the MIL system Metagol and a Prolog meta-interpreter but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We perform between 1 and 3 bottom-up iterations. Initial facts are built from a sample of size 1 of the positive examples. We set the size of the top-down learner search space to n [2, 4] clauses. For k [0, 3], k bottom-up iterations are performed. [...] A timeout is set to 10 minutes. |