NeurASP: Embracing Neural Networks into Answer Set Programming
Authors: Zhun Yang, Adam Ishay, Joohyung Lee
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 1 compares Accidentify of each of Midentify, Neur ASP program Πsudoku \ r with Midentify, Neur ASP program Πsudoku with Midentify, as well as Accsol of Πsudoku with Midentify. All experiments in Section 4 were done on Ubuntu 18.04.2 LTS with two 10-cores CPU Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz and four GP104 [Ge Force GTX 1080]. |
| Researcher Affiliation | Collaboration | Zhun Yang1 , Adam Ishay1 and Joohyung Lee1 2 1 Arizona State University, Tempe, AZ, USA 2 Samsung Research, Seoul, South Korea |
| Pseudocode | No | The paper provides examples of ASP rules but does not contain a dedicated pseudocode or algorithm block. |
| Open Source Code | Yes | The implementation of Neur ASP, as well as the codes used for the experiments, is publicly available online at https://github.com/azreasoners/Neur ASP. |
| Open Datasets | Yes | For comparison, we use the same dataset and the same structure of the neural network model used in [Manhaeve et al., 2018] to train the digit classifier Mdigit in Πdigit. The network Midentify is pretrained using image, label pairs, where each image is a Sudoku board image generated by Open Sky Sudoku Generator (http://www.opensky.ca/ jdhildeb/software/sudokugen/) and We use the dataset from [Xu et al., 2018]. |
| Dataset Splits | Yes | The dataset is divided into 60/20/20 train/validation/test examples. |
| Hardware Specification | Yes | All experiments in Section 4 were done on Ubuntu 18.04.2 LTS with two 10-cores CPU Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz and four GP104 [Ge Force GTX 1080]. |
| Software Dependencies | No | The paper mentions using 'Py Torch' and 'CLINGO' but does not specify their version numbers. |
| Experiment Setup | No | The paper mentions the number of epochs for training (e.g., '63 epochs of training', '500 epochs of training') and general neural network architectures (e.g., '5-layer Multi-Layer Perceptron'), but it does not provide specific hyperparameter values like learning rate, batch size, or optimizer settings within the main text. |