Adversarial Problems

Artificial Intelligence
  • 12 Videos | 39m 48s
  • Includes Assessment
  • Earns a Badge
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Many problems occur in environments with more than one agent, such as games. Explore techniques used to solve adversarial problems to make agents play games, like chess.

WHAT YOU WILL LEARN

  • 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
    build a full high-level representation and solution for an adversarial game using the Minimax Algorithm and Alpha-Beta Pruning

IN THIS COURSE

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