Artificial Intelligence applied in Games (AI for Enterprise Virtual User Group)
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The document presents an introduction to the application of artificial intelligence in games, specifically highlighting the gaming industry's growth and the significance of AI technologies like machine learning and data mining. It discusses datasets useful for AI projects and provides a case study on Super Mario Maker, focusing on identifying game influencers through data analysis methods. The conclusion emphasizes creativity and iterative processes in AI applications within the gaming context.
Artificial Intelligence applied in Games (AI for Enterprise Virtual User Group)
- 1. Introduction to Artificial Intelligence applied in Games Leonardo Mauro P. Moraes Machine Learning Engineer
- 2. Speaker Leonardo Mauro P. Moraes 2 Experience • Machine Learning Engineer (Sinch) Brazil, Nov. 2020 – Now • Data Science Tutor (USP) Brazil, Set. 2020 – Now • PhD in Computer Sciences (USP) Brazil, Mar. 2022 (Doing) https://www.linkedin.com/in/leomaurodesenv/
- 3. 3 Agenda • Games universe • AI introduction Introduction Datasets Data forArtificial Intelligence projects Project Case Study - Super Mario Maker ARTIFICIAL INTELLIGENCE
- 6. • The most popular form of entertainment, reaching millions of players; Digital Games 6 ¹Newzoo’s Games Trends to Watch in 2021 • Universe of games is in constant ascendancy; both in production and in consumption. • Estimated U$ 189.3 billion for 2021¹; • Also, it is cool!
- 7. 7 • eSports - professional competitions. • joining Olympics in 2024. • Streamers - produce online videos. • audience hit 728.8 million in 2021² • +10.0% from 2020. Digital Games • Game Influencers • professional players and streamers. ²How Big Is the Game Live Streaming Audience?
- 8. 8 • Digital influencers of games; • Exist since the popularization of social media; • Publish online content; • e.g., videos, blogs, forums. • High influence in new trends. Game Influencers
- 10. Artificial Intelligence Areas 10 • Artificial Intelligence • Data Mining • Machine Learning • Deep Learning
- 11. Artificial Intelligence (AI) 11 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) Russell, S. J., Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited.
- 12. Data Mining (DM) 12 is the process of discovering patterns in data sets (or datasets) involving methods of machine learning, statistics, and database systems, i.e., Artificial Intelligence; DM focus on extraction of patterns in datasets. Han and Kamber (2011) Han, J., Pei, J., Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
- 13. Machine Learning (ML) 13 is a research area from computer science that can learn automatically through experience and by the use of data. Mitchell (1997) Mitchell, Tom (1997). Machine Learning. New York: McGraw Hill.
- 14. Many terms... 14 Artificial Intelligence Vs Machine Learning Vs Data science Vs Deep learning.
- 15. 15 Fayyad, U. M., Piatetsky-Shapiro, G. S., & Smyth, P. (1996). P. and Uthurusamy, R. Advances in Knowledge Discovery and Data Mining. Knowledge Discovery in Databases (KDD)
- 16. 3. Datasets
- 17. Where to find a dataset? 17 • UCI Machine Learning Repository http://archive.ics.uci.edu/ml/index.php • Kaggle https://www.kaggle.com/datasets • GitHub https://github.com • etc...
- 18. 18 and ... games' dataset? Awesome Game Datasets https://github.com/leomaurodesenv/game-datasets Over 150 contents “Awesome manifesto” is about finding the awesome in the everyday.
- 20. Super Mario Maker 20 • Released on September 2015 - Nintendo • Contains games from Super Mario Bros series. • Video game console - Nintendo Wii U • Player can: • Create a game level; • Play, clear, like …
- 21. Super Mario Maker 21 "If you played every level in #SuperMarioMaker for 1 minute each, it would take you nearly 14 years to play them all!" Nintendo (Twitter)
- 22. Super Mario Maker Styles 22 Super Mario Maker - Nintendo Wii U
- 23. 23 SMMnet - kaggle • over 115k games levels; • over 880k players; • over 7 millions of interactions. • four game difficulties; • four game styles; • four countries (BR, CA, FR, DE); • by quasi five months. Moraes,L. M. P., Codeiro,R. L. F. (2019). Smmnet:A socialnetwork ofgamesdataset.Brazilian Symposium on Databases(SBBD) - DatasetShowcase Workshop (DSW).Ceará,Brazil. Super Mario Maker Dataset
- 24. Super Mario Maker Dataset 24 Social Network of Games • Interactions: player → game • play, like, etc. • They can change over time Moraes,L. M. P., Codeiro,R. L. F. (2019). Smmnet:A socialnetwork ofgamesdataset.BrazilianSymposium on Databases (SBBD) - DatasetShowcaseWorkshop(DSW). Ceará,Brazil.
- 25. 5. Project
- 26. 26 Project Procedures (a) Planning • What to do? PDCA Cycle (plan–do–check–adjust) Proposed by Shewhart, Executed by Deming For quality control (b) Execution • How to do? (c) Evaluation • Did I do it right?
- 27. 27 (a) Game Influencers • Popular players who have high influence in new trends. Consequently • Companies invest to endorse their products; • Direct relevance in viral marketing. Planning
- 28. 28 1. What to do? • I want to find game influencers in Super Mario Maker. (Problem) 2. Has anyone done something similar? • Probably yes. Planning (a) Game Influencers
- 29. 29 • How to detect an influencer? • What are the influencers’ characteristics? Characteristics • Publish many contents; • Receive many “likes”; but not only that... • Evolution of likes, trend of peaks. Planning (a) Game Influencers
- 30. • Evolution of likes, trend of peaks. Planning 30 (a) Game Influencers
- 31. 31 Execution 1. How to do? • Baseline – replicate similar works. 2. Enjoy! • Creativity, explore your ideas! Planning (b) Execution
- 32. 32 1. Modeling • Evolution of likes for each game level. 2. Feature Extraction • Formulas to extract the tendency of peaks in the streams. Execution Planning (b) Execution
- 34. 34 3. Player Modeling • Extract features from the game levels; • A player is represented by the combination of the characteristics of his/her games. Execution Planning (b) Execution
- 36. 36 Did I do it right? • Evaluation, Metrics… Detection -> Classification • Accuracy • Precision • Recall • etc… Execution Evaluation Planning (c) Evaluation
- 37. 37 Dataset • Canadian players - test/train. • Manually labeled the top-100 by the likes. • 41 game influencers. • Consensus: influencer who published in popular websites of the SMM community. Execution Evaluation Planning (c) Evaluation
- 38. 38 • 28 algorithms evaluated; • best: 87.1% accuracy and 90.3% precision. Generality Test • Is an algorithm trained in one country (CA) capable of inferring influencers in another nationality (FR)? Yes, with 77.8% precision. Execution Evaluation Planning (c) Evaluation
- 39. 39 Is this the best way? • I doubt it! Remember: Cycle! • Improve as much as you can. Execution Evaluation Planning (d) Adjust
- 40. 6. Conclusion
- 41. 41 1. How to work with Artificial Intelligence • Awesome Game Datasets Moraes,L. M. P., Codeiro,R. L. F. (2019). Smmnet:A socialnetwork ofgamesdataset.BrazilianSymposium on Databases (SBBD) - DatasetShowcaseWorkshop(DSW). Ceará,Brazil. Moraes,L. M. P., Codeiro,R. L. F. (2019).Detecting Influencers in Very Large Social Networks ofGames.In 21stInternational Conferenceon Enterprise InformationSystems (ICEIS),Crete, Greece. 3. Your creativity is the limit! 2. Case Study - Super Mario Maker (code) • Planning, Execution, Evaluation, Again?
- 43. Thank you for your attention ✌ Leonardo Mauro P. Moraes http://leonardomauro.com /in/leomaurodesenv/