Playgol: Learning Programs Through Play
Authors: Andrew Cropper
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally test our claim on two domains: robot planning and real-world string transformations. Our experimental results suggest that playing can substantially improve learning performance. |
| Researcher Affiliation | Academia | Andrew Cropper University of Oxford andrew.cropper@cs.ox.ac.uk |
| Pseudocode | Yes | Algorithm 1 shows the Playgol algorithm |
| Open Source Code | No | The paper mentions that Playgol is implemented and uses Metagol (with a link to Metagol's GitHub), but it does not provide an explicit statement or link for the Playgol source code itself. |
| Open Datasets | Yes | Our dataset is based on the dataset from [Lin et al., 2014], which in turn is based on [Gulwani, 2011]. |
| Dataset Splits | No | The paper specifies 5 training examples and 5 testing examples per task in the string transformation experiment, but does not mention a separate validation split or explicit percentages for the splits. |
| Hardware Specification | No | The paper mentions a timeout of 60 seconds per task but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper states 'Playgol uses Metagol [Cropper and Muggleton, 2016b]', but it does not specify a version number for Metagol or any other software dependencies. |
| Experiment Setup | Yes | For each p in {0, 200, 400, . . . , 2000}, we call playgol(Xn,BK,Tb,p,5) which returns a set of programs Pp. We enforce a timeout of 60 seconds per play and build task. |