Adversarial Problems

Artificial Intelligence    |    Beginner
  • 12 videos | 34m 18s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 76 users Rating 4.3 of 76 users (76)
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

  • 3m 52s
    After completing this video, you will be able to describe adversarial problems and the challenges they impose on AI. FREE ACCESS
  • 3m 45s
    After completing this video, you will be able to specify how to represent an adversarial problem. FREE ACCESS
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    3.  Minimax Algorithm
    1m 46s
    After completing this video, you will be able to describe how to use the minimax algorithm to play an adversarial game and some of its shortcomings. FREE ACCESS
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    4.  Alpha-Beta Pruning
    2m 40s
    Upon completion of this video, you will be able to describe how to use alpha-beta pruning to improve the performance of the minimax algorithm. FREE ACCESS
  • Locked
    5.  Evaluation Functions
    2m 44s
    Upon completion of this video, you will be able to describe evaluation functions. FREE ACCESS
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    6.  Cutoff Search
    3m 42s
    After completing this video, you will be able to describe how to use cutoffs to perform adversarial searches under a time constraint. FREE ACCESS
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    7.  Lookup Tables
    2m 40s
    Upon completion of this video, you will be able to describe how lookup tables can improve an agent's performance. FREE ACCESS
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    8.  The Game of Chess
    2m 52s
    Upon completion of this video, you will be able to describe chess and how agents can be made to play the game of chess. FREE ACCESS
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    9.  Expectiminimax Value
    3m 1s
    After completing this video, you will be able to describe expectiminimax values in stochastic games and how they make solution searching more difficult. FREE ACCESS
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    10.  Stochastic Evaluation Functions
    2m 37s
    After completing this video, you will be able to describe different evaluation functions that can be used to search for a stochastic game. FREE ACCESS
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    11.  Monte Carlo Tree Search
    2m 36s
    After completing this video, you will be able to describe how to use monte carlo simulations to make decisions when searching. FREE ACCESS
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    12.  Exercise: Use Minimax and Pruning to Play a Game
    2m 4s
    During this video, you will learn how to build a full high-level representation and solution for an adversarial game using the Minimax Algorithm and Alpha-Beta Pruning. FREE ACCESS

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