A curated list of awesome game datasets, and tools to artificial intelligence in games

Overview

🎮 Awesome Game Datasets Awesome

In computer science, Artificial Intelligence (AI) is intelligence demonstrated by machines. Its definition, AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that achieving its goals Russell et. al (2016).

Withal, Data Mining (DM) is the process of discovering patterns in data sets (or datasets) involving methods of machine learning, statistics, and database systems; DM focus on extract the information of datasets Han (2011).

This repository serves as a guide for anyone who wants to work with Artificial Intelligence or Data Mining applied in digital games! Here you will find a series of datasets, tools and materials available to build your application or dataset.

Contributing

Any suggestions or doubts, please open an "issue". If you want to contribute, read this and make a "pull request".


Contents


API

API is "a set of functions and procedures allowing the creation of applications that access the features or data of an operating system, application, or other service" (Google).


Artificial Intelligence

Mobile

Web


Books

  • Drachen, A. Mirza-Babaei, P. Nacke, L. (2018). Games user research. Oxford.
  • El-Nasr, S. Drachen, A. Canossa, A. (2013). Game analytics: maximizing the value of player data. Sprigner.
  • Han, J., Pei, J., Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
  • Hennig-Thurau, T. Houston, M. (2018). Entertainment science: data analytics and practical theory for movies, games, music and books. Springer.
  • Loh, A. Sheng, Y. Ifenthaler, D. (2015). Serious games analytics: methodologies for performance measurement, assessment, and improvement. Springer.
  • Russell, S. J., Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited.
  • Yannakakis, G. N., Togelius, J. (2018). Artificial intelligence and games. Springer.

Dataset

Related


Market Research


Miscellaneous


License

Creative Commons License

Owner
Leonardo Mauro
Data Scientist | Professor (Data Mining, Machine Learning, Business Intelligence).
Leonardo Mauro
Vector.ai assignment

fabio-tests-nisargatman Low Level Approach: ###Tables: continents: id*, name, population, area, createdAt, updatedAt countries: id*, name, population,

Ravi Pullagurla 1 Nov 09, 2021
Garbage classification using structure data.

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wenqi 1 Dec 10, 2021
Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring

Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring (to appear at AAAI 2022) We propose a machine-learning-bas

YunzhuangS 2 May 02, 2022
Language Used: Python . Made in Jupyter(Anaconda) notebook.

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Systematic generalisation with group invariant predictions

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A scikit-learn compatible neural network library that wraps PyTorch

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PyTorch implementation of the Flow Gaussian Mixture Model (FlowGMM) model from our paper

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Source code for Task-Aware Variational Adversarial Active Learning

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Source code for CVPR 2021 paper "Riggable 3D Face Reconstruction via In-Network Optimization"

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YOLOv5 Series Multi-backbone, Pruning and quantization Compression Tool Box.

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AI-Fitness-Tracker - AI Fitness Tracker With Python

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DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models

DSEE Codes for [Preprint] DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models Xuxi Chen, Tianlong Chen, Yu Cheng, Weizhu Ch

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Repository for the semantic WMI loss

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Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently

Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently This repository is the official implementat

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Official implementation for the paper: Multi-label Classification with Partial Annotations using Class-aware Selective Loss

Multi-label Classification with Partial Annotations using Class-aware Selective Loss Paper | Pretrained models Official PyTorch Implementation Emanuel

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Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Image Crop Analysis This is a repo for the code used for reproducing our Image Crop Analysis paper as shared on our blog post. If you plan to use this

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Code release for NeRF (Neural Radiance Fields)

NeRF: Neural Radiance Fields Project Page | Video | Paper | Data Tensorflow implementation of optimizing a neural representation for a single scene an

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Official Pytorch implementation for video neural representation (NeRV)

NeRV: Neural Representations for Videos (NeurIPS 2021) Project Page | Paper | UVG Data Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav S

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MEND: Model Editing Networks using Gradient Decomposition

MEND: Model Editing Networks using Gradient Decomposition Setup Environment This codebase uses Python 3.7.9. Other versions may work as well. Create a

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A tutorial on DataFrames.jl prepared for JuliaCon2021

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