Supplementary materials for “Active Learning Prototypes for Teaching Game AI” Overview This package contains the following files and folders: LICENSE_CODE.txt - The license for the included code. LICENSE_DATA.txt - The license for the included data. README.md - Package description. requirements.txt- Specifies the Python dependencies required for running the included notebook. survey_analysis.ipynb - Notebook used for analyzing the survey data. survey_data.ods- Fully anonymized survey data. Reproducibility of results The results presented in the research paper “Active Learning Prototypes for Teaching Game AI”, published in the Proceedings of the IEEE Conference on Games 2023, and authored by Nuno Fachada, Filipa F. Barreiros, Phil Lopes and Micaela Fonseca, can be reproduced with the Jupyter notebook included in this package. Licenses The code in the Jupyter Notebook is made available under the MIT license (see LICENSE_CODE.txt). The non-code materials are made available under a CC-BY 4.0 license (see LICENSE_OTHER.txt).

Supplementary materials for “Active Learning Prototypes for Teaching Game AI” Overview This package contains the following files and folders: LICENSE_CODE.txt - The license for the included code. LICENSE_DATA.txt - The license for the included data. README.md - Package description. requirements.txt- Specifies the Python dependencies required for running the included notebook. survey_analysis.ipynb - Notebook used for analyzing the survey data. survey_data.ods- Fully anonymized survey data. Reproducibility of results The results presented in the research paper “Active Learning Prototypes for Teaching Game AI”, published in the Proceedings of the IEEE Conference on Games 2023, and authored by Nuno Fachada, Filipa F. Barreiros, Phil Lopes and Micaela Fonseca, can be reproduced with the Jupyter notebook included in this package. Licenses The code in the Jupyter Notebook is made available under the MIT license (see LICENSE_CODE.txt). The non-code materials are made available under a CC-BY 4.0 license (see LICENSE_OTHER.txt).

Overview This dataset contains 10,917 news articles with hierarchical news categories collected between January 1st 2019, and December 31st 2019 classified by using NewsCodes Media Topic taxonomy. We manually labelled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news. Reproducibility of results The results presented in the research paper "MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification", technical validation can be reproduced using functions in github repository. Licenses The dataset is made available under a CC-BY 4.0 license (see LICENSE_DATA.txt).

Overview This dataset contains 10,917 news articles with hierarchical news categories collected between January 1st 2019, and December 31st 2019 classified by using NewsCodes Media Topic taxonomy. We manually labelled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news. Reproducibility of results The results presented in the research paper "MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification", technical validation can be reproduced using functions in github repository. Licenses The dataset is made available under a CC-BY 4.0 license (see LICENSE_DATA.txt).

Overview This dataset contains performance and navigation benchmarks obtained by generating eight maps with the snappable meshes algorithm multiple times. Data in columns is organized as follows: run - Number of generation run. genset - Generated map (maps "(a)" to "(h)" from the Benchmark scene. navset- Number of navigation points used to generate the navigation metrics. tg - Duration of the map generation process, in milliseconds. tv - Duration of the map validation process, in milliseconds. c - Average percentage of valid connections between navigation points. ar - Relative area of the largest fully-connected (i.e., fully-navigable) region. nclu - Number of isolated regions, i.e., of regions which are not connected to any another. genseed - Seed used for the generation process. navseed - Seed used for determining the validation metrics. A number of results presented in the research paper "Procedural Generation of 3D Maps with Snappable Meshes" are obtained from this dataset. Reproducibility of results The results presented in the research paper "Procedural Generation of 3D Maps with Snappable Meshes", namely in Figure 8 and Figure 10, can be reproduced with the Jupyter notebook included with this dataset (file analysis.ipynb). Licenses The dataset is made available under a CC-BY 4.0 license (see LICENSE_DATA.txt). The code in the Jupyter Notebook is made available under the MIT license (see LICENSE_CODE.txt).

Overview This dataset contains performance and navigation benchmarks obtained by generating eight maps with the snappable meshes algorithm multiple times. Data in columns is organized as follows: run - Number of generation run. genset - Generated map (maps "(a)" to "(h)" from the Benchmark scene. navset- Number of navigation points used to generate the navigation metrics. tg - Duration of the map generation process, in milliseconds. tv - Duration of the map validation process, in milliseconds. c - Average percentage of valid connections between navigation points. ar - Relative area of the largest fully-connected (i.e., fully-navigable) region. nclu - Number of isolated regions, i.e., of regions which are not connected to any another. genseed - Seed used for the generation process. navseed - Seed used for determining the validation metrics. A number of results presented in the research paper "Procedural Generation of 3D Maps with Snappable Meshes" are obtained from this dataset. Reproducibility of results The results presented in the research paper "Procedural Generation of 3D Maps with Snappable Meshes", namely in Figure 8 and Figure 10, can be reproduced with the Jupyter notebook included with this dataset (file analysis.ipynb). Licenses The dataset is made available under a CC-BY 4.0 license (see LICENSE_DATA.txt). The code in the Jupyter Notebook is made available under the MIT license (see LICENSE_CODE.txt).

Overview This dataset contains grades (0-20 scale) given to students in the context of the ColorShapeLinks AI assignment during the two semesters of the 2019/20 academic year. A number of results presented in the research paper "ColorShapeLinks: A Board Game AI Competition for Educators and Students" are obtained from this dataset. Reproducibility of results The results presented in the research paper "ColorShapeLinks: A Board Game AI Competition for Educators and Students", namely in Figure 5 and Table 4, can be reproduced with the Jupyter notebook included with this dataset (file analysis.ipynb). Licenses The dataset is made available under a CC-BY 4.0 license (see LICENSE_DATA.txt). The code in the Jupyter Notebook is made available under the MIT license (see LICENSE_CODE.txt).

Overview This dataset contains grades (0-20 scale) given to students in the context of the ColorShapeLinks AI assignment during the two semesters of the 2019/20 academic year. A number of results presented in the research paper "ColorShapeLinks: A Board Game AI Competition for Educators and Students" are obtained from this dataset. Reproducibility of results The results presented in the research paper "ColorShapeLinks: A Board Game AI Competition for Educators and Students", namely in Figure 5 and Table 4, can be reproduced with the Jupyter notebook included with this dataset (file analysis.ipynb). Licenses The dataset is made available under a CC-BY 4.0 license (see LICENSE_DATA.txt). The code in the Jupyter Notebook is made available under the MIT license (see LICENSE_CODE.txt).