• SOURAV KUMAR BEHERA
• Md Nashiruddin Parvez
• Umang Sharma
• Geetesh Singh
• S. Abinayaa

This paper describes the evolution of a genetic program to optimize a problem featuring task prioritization in a dynamic, randomly updated environment. The specific problem approached is the "snake game" in which a snake confined to a rectangular board attempts to avoid the walls and its own body while eating pieces of food. The problem is particularly interesting because as the snake eats the food, its body grows, causing the space through which the snake can navigate to become more confined. Furthermore, with each piece of food eaten, a new piece of food is generated in a random location in the playing field, adding an element of uncertainty to the program. This paper will focus on the development and analysis of a successful function set that will allow the evolution of a genetic program that causes the snake to eat the maximum possible pieces of food.

AI, Machine Learning, Neural Network, Logical Reasoning

"AI LEARNING ON SNAKE PATTERN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.220-223, April-2019, Available :http://www.jetir.org/papers/JETIR1904934.pdf

"AI LEARNING ON SNAKE PATTERN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp220-223, April-2019, Available at : http://www.jetir.org/papers/JETIR1904934.pdf