A Tool to Graphically Edit CP-Nets
Authors: Aidan Shafran, Sam Saarinen, Judy Goldsmith
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The software package presented allows editing of CP-nets through a graphical interface, loads and saves to an XML-based file format, and detects properties of the currently loaded CP-net. |
| Researcher Affiliation | Academia | Aidan Shafran, Sam Saarinen University of Kentucky Lexington, KY 40506 samuel.saarinen@uky.edu; Judy Goldsmith University of Kentucky Lexington, KY 40506 goldsmit@cs.uky.edu |
| Pseudocode | No | The paper describes the functionality of the software but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The visualizer is provided open-source and free of charge under the MIT License, as are most of the third-party libraries it uses. [...] It is written in Java Script and packaged as a webpage that can be accessed on a website http: //cs.uky.edu/ goldsmit/visualizer/ or saved locally. |
| Open Datasets | No | The paper describes a software tool for editing CP-nets and does not involve training machine learning models or using datasets for training purposes. |
| Dataset Splits | No | The paper describes a software tool and does not involve validation datasets or splits for model validation. |
| Hardware Specification | No | The paper states the tool "can be run in most web browsers on most platforms," but does not provide any specific hardware details such as CPU/GPU models, memory, or specific computer specifications used for development or demonstration. |
| Software Dependencies | No | The paper mentions "It uses multiple third-party open-source Java Script libraries, including D3 and Web Co La." However, it does not provide specific version numbers for these libraries, which is required for reproducibility. |
| Experiment Setup | No | The paper describes the features and interface of a software tool, not an experimental setup with hyperparameters or training configurations for a computational model. |