Adversarial Problems
Artificial Intelligence
12 Videos | 39m 48s - 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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1.Adversarial Problems3m 52sUP NEXT
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2.Adversarial Problem Representation3m 45s
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3.Minimax Algorithm1m 46s
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4.Alpha-Beta Pruning2m 40s
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5.Evaluation Functions2m 44s
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6.Cutoff Search3m 42s
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7.Lookup Tables2m 40s
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8.The Game of Chess2m 52s
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9.Expectiminimax Value3m 1s
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10.Stochastic Evaluation Functions2m 37s
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11.Monte Carlo Tree Search2m 36s
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12.Exercise: Use Minimax and Pruning to Play a Game2m 4s
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