Gameplay Cognitive Decision Support Using Statistical and Non-statistical Parametric Fusion

Surprises are an inevitable part of any gaming application. These surprises eventually make decision-making a tough task. Cognitive learning always works in actions and associative responses. Gaming and cognition have a close association while decision-making in gaming or economics applications. These applications always respond to opponents’ moves using statistical variants. These responses are always affected due to surprises. These surprises contribute to increasing uncertainty in decision-making. These surprises are not quantified and considered before action or response selection. This research is focusing on hypotheses as using non-statistical information such surprises can be optimized. There is also a need to associate quantified surprises and associated statistical variants. Here the model is proposed using data fusion techniques to use statistical and non-statistical parameters. This intern improves cognitive decision-making. The system can be adapted to other applications like gamification, economics applications, etc.