A Framework for Designing Behaviour Tree Artificial Intelligent Game Based on Dynamic Human Emotions

An adaptive game design includes human emotions as a key factor for selecting the next level of harness in the game design. In most of the situation, face expression could not identified exactly and this may leads to an uncertainty for selecting a next level in the game flow. An efficient game design requires an efficient behaviour tree construction model based on the emotions of a player. This paper presented an artificial intelligent based game design using behaviour tree model by including an efficient emotion detection or classification system using a deep learning model ResNet 50. The proposed technique classifies the emotion of a player based five different category and the player current state of mind will be calculated based on this emotion score. The Behavior tree has been constructed from the foundation based on the hardness value calculated for each sub-BT. The performance evaluation for the emotion classification archives close to 92% and this accuracy will lead to construct an efficient BT for any game. We have evaluated the emotion detection system with other related Deep learning models.