by Devangini Patel | Released October 2017 | ISBN: 9781788476539

Harness the power of AI to solve and play powerful and smarter puzzles and games by itself and against humans!About This VideoEnter the world of games with AIComprehensive, fast, and friendly guide to implementing AI in your games and puzzlesUnderstand how to leverage different player and search strategies to make your algorithms smarterIn DetailIn 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. Show and hide more

1. Chapter 1 : Constraint Satisfaction Problem
   • The Course Overview 00:06:59
   • Installing Python and Its Libraries 00:08:44
   • Recap of DFS 00:07:28
   • Introduction to Coloring Application 00:02:39
   • Constraint Satisfaction Problem Formulation 00:07:24
   • Constraint Satisfaction Problem Formulation (Continued) 00:04:40
   • DFS to Backtracking Search 00:13:18
   • Using Heuristics in Backtracking Search 00:10:56
   • Forward Checking 00:07:46
   • Arc Consistency 00:11:02
2. Chapter 2 : Using AI to Play Games
   • Introduction to Tic-Tac-Toe Game 00:03:07
   • Understanding a Game Move 00:04:32
   • Game Search Formulation 00:15:46
   • Score Evaluation 00:06:04
   • Depth Limited Search 00:07:27
   • Minimax Search 00:12:05
   • Tic-Tac-Toe Game 00:12:17
   • Alpha-Beta Pruning 00:12:07
3. Chapter 3 : Evolutionary Search
   • Introduction to N Queens Puzzle 00:05:01
   • Population 00:06:21
   • Fitness and Selection 00:10:00
   • Variation Operations 00:08:16
   • Termination 00:06:01
   • DIY 00:07:12
   • Comparison with Other Methods 00:04:22
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