Date post: | 21-Dec-2015 |
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Outline
Introduction Case-based Reasoning (CBR) System Architecture Experiment Evaluation Conclusion
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
Case-based Reasoning (CBR)
Case-based reasoning (CBR) is the process of solving new problems based on the solutions of similar past problems
Similarity function
Similarity (T, S ) =
f (Ti, Si ) = (1 - |Ti – Si | / 15) * 4
25
1
( , )i
f Ti Si
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
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
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