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Many problems occur in environments with more than one agent, such as games. In this course, you will learn some techniques used to solve adversarial problems to make agents play games, like chess.
start the course
describe adversarial problems and the challenges they impose on AI
specify how to represent an adversarial problem
describe how to use the minimax algorithm to play an adversarial game and some of its shortcomings
describe how to use alpha-beta pruning to improve the performance of the minimax algorithm
describe evaluation functions
describe how to use cutoffs to be able to perform adversarial searches under a time constraint
describe how lookup tables can be used to improve an agent's performance
describe chess and how agents can be made to play the game of chess
describe expectiminimax values in stochastic games and how they make solution searching harder
describe different evaluation functions that can be used to search in a stochastic game
describe how to use monte carlo simulations to make decisions when searching
Practice: Using the Minimax Algorithm
build a full high-level representation and solution for an adversarial game using the minimax algorithm and alpha-beta pruning
The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.