Implementing AI to Play Games هذه الدورة تدرس باللغة الإنجليزية

تفاصيل الدورة

In video games, Artificial Intelligence is used to generate responsive or intelligent behavior primarily in Non-Player Characters (NPCs), like human intelligence. In this course, we look at games; we understand how to decide which move to take based on future possibilities and payoffs (just as, in chess, we look n-moves ahead into the future).

We explore how to solve applications where there are a number of parameters to optimize, such as time or distance, and the possibilities are exponential. We look at how to design the various stage of the evolutionary algorithm that will control performance. We take a sample game—Tic-Tac-Toe—and show how various steps of the algorithm are implemented in code. And we look at color filling as a constraint satisfaction application and see how various algorithm concepts are applied in code.

Finally, we also explain a trip-planning application and see how the application is solved through evolutionary algorithms.

About the Author

Devangini Patel is a Ph.D. student at the National University of Singapore, Singapore. Her research interests include Deep Learning, Computer Vision, Machine Learning, and Artificial Intelligence. She has completed her masters in Artificial Intelligence from the University of Southampton, UK. She has over 5 years' experience in the field of AI and has worked on various industrial and research projects in AI including facial expression analysis, robotics, virtual try-on, object recognition and detection, and advertisement ranking.