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 [1].
An Online Logic Programming Development Environment
Authors: Christian Reotutar, Mbathio Diagne, Evgenii Balai, Edward Wertz, Peter Lee, Shao-Lon Yeh, Yuanlin Zhang
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We developed an online answer set programming environment with simple interface and self contained ο¬le system. It is expected to make the teaching of answer set programming more effective and help us to reach more students. ... Our online environment, with a carefully designed simple interface and a self contained ο¬le system, provided easy access and sharing, reduced the learning curve, and removed the installation and maintenance expenses, by our experience of using it in our teaching in Fall 2015. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Johns Hopkins University, USA 2Department of Mathematics, Minneapolis Community and Technical College, USA 3Department of Computer Science, Texas Tech University, USA 4Department of EECS, University of California, Berkeley, USA 5Lubbock High School, Lubbock, Texas, USA |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper mentions the system is 'avail at http://goo.gl/uk SZET' which appears to be a link to the live application, not its source code. There is no explicit statement of source code release. |
| Open Datasets | No | The paper describes the development of an online environment and its use in teaching, not experiments involving datasets with training splits. |
| Dataset Splits | No | The paper does not mention any validation splits or a validation dataset. |
| Hardware Specification | No | No specific hardware used for running experiments or developing the system is mentioned. |
| Software Dependencies | No | The paper mentions 'front end (FE) in Java Script, processing unit (PU) in PHP, and back end (BE) (SPARC solver (Balai, Gelfond, and Zhang 2013) and My SQL database system)' but does not provide specific version numbers for these software components. |
| Experiment Setup | No | The paper describes the system's design and its observed benefits in a teaching context, but it does not detail an experimental setup with hyperparameters or specific training configurations typical of machine learning or simulation experiments. |