
## Conclusion

We believes that modeling the human behavior has two big advantages of being able to learn from human-played examples so that it can generate a behavior (control the character) as well as predict what will the other players do. Learning from game sequences with a human model allows for fast development of realistic AI in video games. It also decreases the complexity from having many possible actions and possible behaviors thanks to the behavior learning part. Applying the model to other players for prediction enables the robot to have these predictions as input variables, and act accordingly ("I think that you think"...). Human players do long term planning, consciously or not. However, modeling high-level cognition is very hard. We assume that by modeling a reactive robot with regard to observable variables and predicted variables, we can emulate human planning. In particular, some variables are future extrapolated values, some others encode past states and, in the future, we plan to have variables corresponding to more elaborated predicted states through other Bayesian programs. 

![0_image_0.png](0_image_0.png)

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