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Teaching Game AI from Scratch: A Comprehensive Guide for Beginners and Intermediates

This resource offers insights from Dr. Brian Magerko on teaching game AI from the ground up, blending theory and practical applications. It highlights the importance of appropriate abstraction levels for both beginners and intermediates, covering a wide array of techniques and algorithms. Emphasizing project-based learning, students engage in hands-on projects that encourage self-selected goals, experimentation, and teamwork. The course design focuses on integrating soft skills and aligning lecture material with real-world projects, ensuring an enriched learning experience.

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Teaching Game AI from Scratch: A Comprehensive Guide for Beginners and Intermediates

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  1. How to Teach Game AI from ScratchBrian Magerko, Ph.D.Assistant Professor of Digital MediaGeorgia Tech

  2. A Plethora of Programs • Computational Media (B.S) • Computer Science (B.S., M.S., Ph.D.) • Digital Media (M.S., Ph.D.) • Human-Centered Computing (Ph.D.)

  3. Student Experience • Motivation? • Coding experience? • AI background?

  4. Course Design • Appropriate abstraction for beginners & intermediates

  5. Course Design • Appropriate abstraction for beginners & intermediates • Breadth of techniques

  6. Course Design • Appropriate abstraction for beginners & intermediates • Breadth of techniques • Algorithms / Aesthetics

  7. spy party

  8. prom week

  9. party quirks AIIDE 2011; IVA 2011

  10. Recommended Texts

  11. Course Design • Appropriate abstraction for beginners & intermediates • Breadth of techniques • Algorithms / Aesthetics • Build early and consistently

  12. Course Design • Appropriate abstraction for beginners & intermediates • Breadth of techniques • Algorithms / Aesthetics • Build early and consistently • Focus on enabling soft skills

  13. Soft Skills • Problem identification • Survey classes of approaches • Matching solutions to problems • Presenting rationale & work • Working in teams

  14. Project-based Learning

  15. Project-based Learning • Lecture material tied to projects

  16. Project-based Learning • Lecture material tied to projects • Uses free game AI resources

  17. project 1

  18. project 2

  19. Google AI Challenge others

  20. Project-based Learning • Lecture material tied to projects • Uses free game AI resources • No algorithmic requirement

  21. Project-based Learning • Lecture material tied to projects • Uses free game AI resources • No algorithmic requirement • Self-selected goal for project

  22. Project-based Learning • Lecture material tied to projects • Uses free game AI resources • No algorithmic requirement • Self-selected goal for project • Experimentation encouraged

  23. Project-based Learning • Lecture material tied to projects • Uses free game AI resources • No algorithmic requirement • Self-selected goal for project • Experimentation encouraged • Process, product, and presentation are evaluated

  24. Project-based Learning • Lecture material tied to projects • Uses free game AI resources • No algorithmic requirement • Self-selected goal for project • Experimentation encouraged • Process, product, and presentation are evaluated • Community of practice

  25. Final Project

  26. Final Project • Team project

  27. Final Project • Team project • AI as Aesthetic vs. Board Game AI

  28. Final Project • Team project • AI as Aesthetic vs. Board Game AI • Open-ended requirements

  29. Student Examples

  30. mapstermind

  31. zombies (h)ate my neighbors

  32. ra

  33. dominion

  34. spy game

  35. power grid

  36. Takeaways • Student-driven projects • Focus on soft skills & breadth • Make use of available environments • Algorithms & Aesthetics

  37. Thanks! magerko@gatech.edu http://lcc.gatech.edu/~bmagerko6 http://adam.cc.gatech.edu

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