Deep Learning model for Chess Game Player

  • Ankur Chaturvedi, Rahul Pradhan


In an era of mass computation, we may have sufficient human resources and required equipment. Still, we are lacking somewhere, we need more accuracy in our work and a faster pace in work is on demand by IT industry. Artificial intelligence is one of the solution of this continuously growing problem. We propose to create some smart machines which are capable of learning the work to be done by Human worker, and do it itself. An idea in which Human creativity and machine capability will combine and create a powerful system will come into existence which will learn like a normal human being but at a faster pace. In addition, a demonstration of advanced technology based on Artificial Intelligence and machine learning. A Reinforcement Learning Algorithm, which later becomes a self-learning AI, and performs up to the mark, Improves itself over time. We have proposed an algorithm called VADER, which can outsmart tradition methods to perform their specific task using Convolution Neural Networks and General Reinforcement Learning methods.

How to Cite
Rahul Pradhan, A. C. (2020). Deep Learning model for Chess Game Player. International Journal of Advanced Science and Technology, 29(3), 5288 - 5298. Retrieved from