Adversarial Problems
Artificial Intelligence
| Beginner
- 12 videos | 34m 18s
- Includes Assessment
- Earns a Badge
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
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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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3m 52sAfter completing this video, you will be able to describe adversarial problems and the challenges they impose on AI. FREE ACCESS
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3m 45sAfter completing this video, you will be able to specify how to represent an adversarial problem. FREE ACCESS
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3. Minimax Algorithm1m 46sAfter 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 Pruning2m 40sUpon 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
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5. Evaluation Functions2m 44sUpon completion of this video, you will be able to describe evaluation functions. FREE ACCESS
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6. Cutoff Search3m 42sAfter 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 Tables2m 40sUpon 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 Chess2m 52sUpon 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 Value3m 1sAfter 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 Functions2m 37sAfter 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 Search2m 36sAfter 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 Game2m 4sDuring 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
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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