Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019 - Game Playing 1 - Minimax, Alpha-beta Pruning | Stanford CS221: AI (Autumn 2019)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Cke8v4 Topics: Minimax, expectimax, Evaluation functions, Alpha-beta pruning Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-liang Assistant Professor Dorsa Sadigh Assistant Professor in the Computer Science Department & Electrical Engineering Department https://profiles.stanford.edu/dorsa-sadigh To follow along with the course schedule and syllabus, visit: https://stanford-cs221.github.io/autumn2019/#schedule 0:00 Introduction 0:43 Course plan 2:09 A simple game 3:29 Roadmap 4:01 Game tree 5:05 Two-player zero-sum games 8:55 Example: chess 11:43 Characteristics of games 22:33 Game evaluation example 29:01 Expectimax example 33:51 Extracting minimax policies 34:21 The halving game 38:44 Face off 45:41 Minimax property 2 48:18 Minimax property 3 53:02 A modified game 53:49 Expectiminimax example 55:26 Expectiminimax recurrence 57:19 Computation