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Motivate AI Class with Interactive Computer Game

Motivate AI Class with Interactive Computer Game. Author ： Akcell Chiang Presented by Yi Cheng Lin. Outline. Introduction Case-based Reasoning (CBR) System Architecture Experiment Evaluation Conclusion. Introduction.

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Motivate AI Class with Interactive Computer Game

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  1. Motivate AI Class with Interactive Computer Game Author：Akcell Chiang Presented by Yi Cheng Lin

  2. Outline • Introduction • Case-based Reasoning (CBR) • System Architecture • Experiment • Evaluation • Conclusion

  3. Introduction • Commercial interactive games is one of the major entertainment in our society, the game annual expense is more than the film industry for a family in average • This paper reports adapting traditional Pacman game with Machine Learning technology Case-based Reasoning (CBR) to provide student learning motivation in the AI subject teaching

  4. Case-based Reasoning (CBR) • Case-based reasoning (CBR) is the process of solving new problems based on the solutions of similar past problems

  5. System Architecture

  6. environment Brick puzzle world, the whole system running-map consists of a 15x15 rectangular grid,

  7. System architecture diagram

  8. System shared running-map diagram

  9. CBR case representation

  10. Similarity function • Similarity (T, S ) = • f (Ti, Si ) = (1 - |Ti–Si | / 15) * 4

  11. Case Acquisition • With 5x5 individual perceptions, the system will have only 5x5x4x4 equals to 400 different results • If the system only adopts 100 cases then the system may only give the CBR agent ¼ chances to find the right move • the study decides to set up an 85% similarity of learning threshold while the study doing the 100 case training

  12. Case Acquisition • For helping CBR agent find better suggestion, the study decides to adopt more cases with higher recognition rate in the bonus less areas • with 200 cases and over 90% learning threshold, at least, the CBR agent will have ½ chance to find the right suggestion

  13. Case database refine • the study finds out there is an overfiting problem of 200 cases CBR agent since it did not has better choice in some critical moves • The study therefore, decides to prune 200 cases of redundancy into 165 refining cases

  14. Experiment

  15. Evaluation 100 cases 200 cases 200 cases

  16. Conclusion • The study expects this temptation is just the beginning for applying computer games in teaching AI and CBR technologies in IT education • The advantage of AI project or assignment is that the experiment has to adopt human competition experience

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