Modeling and verification of the "cause &effect" relation comprehension by neural networks using learning algorithms

The capability of predicting what will happen next based on past experiences and environment features could be thought of as one kind of cause & effect comprehension. We have modeled a system to attain this capability by neural networks using reinforcement and supervised learning algorithms to comprehend the ground shot in a golf game. The system predicts the approximate distance the golf ball runs and we have found that the system works very well and it becomes almost perfect just after practicing with several samples.