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Teaching AI through retro gaming

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“Teaching AI through retro gaming”

Computer Science is in high demand, yet students do not have the necessary motivation and drive to go through theoretic AI concepts. Book examples are outdated, students struggle to understand the use cases. On the other hand students love computer games, and feel a natural drive to grasp the algorithms behind them. Stateof- the-art computer games are nonetheless too complex for beginner students to grasp. Diogo Gomes will explain how he uses classic computer games to teach AI algorithms and keep his students fully motivated.

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Teaching AI through retro gaming

  1. 1. Teaching AI through retro gaming HOW TO MOTIVATE 21ST CENTURY STUDENTS THROUGH 20TH CENTURY GAMES
  2. 2. Computer Science General shortage of CS Engineers worldwide Artificial Intelligence has gained an important momentum Yet very few students finish CS degrees
  3. 3. What problems students tackle in introductory AI? TRAVELLING SALESMAN PROBLEM GOAL STACK PLANNING
  4. 4. Problem: Keeping students motivated
  5. 5. Realising what they are atually doing in class.
  6. 6. Why did you enroll in CS ? 0 50 100 150 200 250 Knowledge General Exp Use in another field Money Sit at computer Programming Computer Games Hardware Exp Friend in field Influences for CS Male Female Lori Carter. 2006. Why students with an apparent aptitude for computer science don't choose to major in computer science
  7. 7. Games & AI • Games have traditionally been one the main research topics in AI • IBM Deepblue was a major landmark in AI research • So was Alpha Go – Google/Deepmind • Do students relate to Chess & Go ? • Not very popular when you are 20-21
  8. 8. Computer Games WHEN STUDENTS THINK OF GAME AI THEY THINK OF: FIFA, CSGO, LOL, FORTNITE, PUBG
  9. 9. Matching expectations AI is an introductory course for 3rd year students Students programming skills are average, but mostly centred in JAVA and Python Existing Game Engines place a large overhead and are not core to the course Frameworks such as OpenSpiel focus in advanced AI techniques (e.g. deeplearning) Simple games can be easily programmed and repurposed for learning objectives (card games, arcade games, Atari, NES)
  10. 10. Running a live experiment After years using a robot simulator (CiberRato) in 2015/16 a first experiment: ◦ Development of Blackjack (cards) playing agents ◦ Students grade proportional to their agent performance (with minimum set of requirements to classify 10/20) ◦ Students could still do the CiberRato assignment from previous years ◦ Results: ◦ Almost all students opted for the Blackjack agent (despite existing code from previous years) ◦ Students were eager to improve their agents
  11. 11. AI skills involved Minimax algorithm Monte-Carlo Simulation Bayesian Networks Some students actually went over the objectives and studied advanced AI algorithms to improve their agents
  12. 12. Improving Grading was very complicated... ◦ ~70 agents that were required to play ~1000 games (to avoid impact of random events and luck) ◦ Agent/Game agent code tightly bound ◦ Agent was a simple Python Class object (students could temper with game engine, extra code review required)
  13. 13. Retro fitting a retro game “Normal” games are not usually accessible to computer AI’s students They are monolithical The UI is bound to the execution environment They are not ready for automated evaluation.
  14. 14. Snakes In 2016/17 we introduced “Snakes” (originally blockade) – made popular by Nokia Graphic Engine based on PyGame Decoupled Agent/UI code from Game Engine (Client-Server Architecture) One-on-One games Scoring based on ranking (used ATP rules)
  15. 15. Snakes • Students could see “live action” • Deathmatch Snakes in 2016/17 • Collaborative Snakes in 2017/18
  16. 16. Snake Some students improved the GUI Difficulties in training (lack of opponents) Synchronous engine meant game could stall due to bad AI algorithms. AI topics: Search algoritms A*, Greedy, Depth-First Jump-point-search Minimax
  17. 17. Pac-Man • New asynchronous engine • Introduced in 2018/19
  18. 18. Pac-Man Viewer is yet another client (can run the game headless) Students get creative, developing strategies to maximize scores (no longer simple path finding algorithms) AI Topics: Search algoritms A*, Greedy, Deapth-First Jump-point-search Minimax
  19. 19. Bomberman • Based on previous year engine • Current edition (2019/20)
  20. 20. Bomberman Server publicly shares high scores Students still contributing UI and develop new strategies AI Topics Planning Search algoritms A*, Greedy, Deapth-First Jump-point-search
  21. 21. Going into detail HOW-TO MAKE A RETRO GAME ACCESSIBLE TO AI STUDENTS
  22. 22. Architecture Games are implemented using a client-server architecture ◦ Server – runs the game logic ◦ Client – runs the AI agent ◦ Viewer – runs the GUI Server provides a websocket interface to both client’s and viewer’s pushing game state update asynchronously. ◦ Client updates the AI agent state and provides actions (key press) ◦ Viewers updates the screen Upon game over the server issues an HTTP POST to a score server containing information relevant for student grading (e.g. score, lives, time, steps, etc)
  23. 23. Representation Model Students get access to the game state through JSON documents ◦ Easily parsable in Python ◦ Human readable (eases debugging) ◦ Well known technology Actions are those of humans ◦ Agents can press a key at any time through a JSON message containing a single key
  24. 24. The role of Gamification A public high score table with KPI’s keeps student progress - Has everyone score minimum to pass? - How many have reached the end of the game - How intelligent is each agent based on KPI’s
  25. 25. Open Source + GitHub The base Project is Open Source (MIT) ◦ https://github.com/dgomes/iia-ia-snakes ◦ https://github.com/dgomes/iia-ia-pacman ◦ https://github.com/dgomes/iia-ia-bomberman Students are encouraged to contribute (extra credits for new features) Evaluation is carried out through GitHub Classroom ◦ Each group has its own Project (private) ◦ On the deadline the GitHub Classroom collects all the code ◦ Server runs the games headless 10x
  26. 26. Conclusion ◦ We didn’t invent any thing new: ◦ http://modelai.gettysburg.edu ◦ We did get students involved with games they never played before  ◦ Attention to class and time spent in the Project increased ◦ Our colleagues from other courses complained  ◦ Some students actually did the next year Project for fun. ◦ Motivation has clearly increased in class 
  27. 27. Future ◦ This class opened a door for many of them ◦ Participation in hackatons such as Global Game Jam increased ◦ Student interest in developing games is high ◦ We need to attend to this interest (Lic, MSc, Phd?) ◦ Students already search for dissertation topics related to AI/Games ◦ Students planning in running their own hackatons.
  28. 28. Questions? dgomes@ua.pt @dgomes

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