Answers.Body: <p>You can do that, however you're probably going to hit the wall that alpha-beta pruning got with Go and large scale games... it does not scale too well</p>
  <p>Depending on what's your definition of <em>when that situation happens again in real life</em> you might need very large and deep trees to get a good estimate, let alone that to work well you have to have a good bounding algorithm, which is not trivial to obtain</p>
  <p>As <a href="https://ai.stackexchange.com/users/1847/neil-slater">Neil</a>, you probably also need to develop a very sophisticated and complex model to simulate your scenario</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-11-21T19:51:21.113Z
Answers.Text: For "real life", the OP would also need a rather sophisticated predictive model for exploring "every possible scenario".
Answers.UserDisplayName: Neil Slater
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Answers.CreationDate: 2023-11-21T19:23:57.293Z
Answers.LastActivityDate: 2023-11-21T20:48:52.290Z
Answers.LastEditDate: 2023-11-21T20:48:52.290Z
Answers.UserDisplayName: Alberto
Answers.Body: <p>It also depends on the type of game.</p>
  <p>The problem with Go is that a board that looks really good can turn into a disaster on the next move.</p>
  <p>Games that have easy evaluations and experience only small incremental changes with each move are much better suited for alpha-beta pruning.</p>
  <p>For instance, Checkers could be trivially evaluated as the difference in number of pieces each player has.
  Most moves will change the value by 0 or 1 points, with double jumps being much less common, and higher numbers being rare.
  Not going just one level deeper is usually not likely to be a significant problem.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-11-21T22:27:40.420Z
Answers.Text: _Checkers could be trivially evaluated as the difference in number of pieces each player has_ that's a pretty greedy policy... you probably need something more than that
Answers.UserDisplayName: Alberto
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-11-22T00:26:48.820Z
Answers.Text: @Alberto, I said it "*could*" be, not because it would be the best way, but because calculating the value would be very efficient. For Go, there is no evaluation even remotely that efficient, which illustrates the fundamental difference that makes alpha-beta appropriate for one but not for the other.
Answers.UserDisplayName: Ray Butterworth
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Answers.UserDisplayName: Ray Butterworth
Body: <p>I had an idea to use the this algorithm to simulate real life game scenarios where a player could train and retrain their mistake and be ready for when that situation happens again in real life.
  This simulation would calculate every possible scenario and give you the best one.
  This would work almost the same way as it is used on Stockfish for chess. It calculates every possible scenario and always makes the right move.</p>
  <p>Now my question is, would that be possible? Is there another algorithm/AI/Software that could make this simulation work?</p>
  
Tags: <game-ai><alpha-beta-pruning><real-time>
Title: Can I use Alpha-Beta Pruning for real life game simulation?
UserDisplayName: Caio Amaral
ThreadText: # Can I use Alpha-Beta Pruning for real life game simulation?
  
  (Asked by: username_0 on 2023-11-21)
  
  I had an idea to use the this algorithm to simulate real life game scenarios where a player could train and retrain their mistake and be ready for when that situation happens again in real life.
  This simulation would calculate every possible scenario and give you the best one.
  This would work almost the same way as it is used on Stockfish for chess. It calculates every possible scenario and always makes the right move.
  
  Now my question is, would that be possible? Is there another algorithm/AI/Software that could make this simulation work?
  
  **Tags:** game-ai, alpha-beta-pruning, real-time
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_3 on 2023-11-21. Score: 3 | Accepted answer
  
  It also depends on the type of game.
  
  The problem with Go is that a board that looks really good can turn into a disaster on the next move.
  
  Games that have easy evaluations and experience only small incremental changes with each move are much better suited for alpha-beta pruning.
  
  For instance, Checkers could be trivially evaluated as the difference in number of pieces each player has.
  Most moves will change the value by 0 or 1 points, with double jumps being much less common, and higher numbers being rare.
  Not going just one level deeper is usually not likely to be a significant problem.
  
  > **Comments:**
  > **username_1**: _Checkers could be trivially evaluated as the difference in number of pieces each player has_ that's a pretty greedy policy... you probably need something more than that  
  > **username_3**: @username_1, I said it "*could*" be, not because it would be the best way, but because calculating the value would be very efficient. For Go, there is no evaluation even remotely that efficient, which illustrates the fundamental difference that makes alpha-beta appropriate for one but not for the other.  
  
  ---
  
  ### Answer by username_1 on 2023-11-21. Score: 0
  
  You can do that, however you're probably going to hit the wall that alpha-beta pruning got with Go and large scale games... it does not scale too well
  
  Depending on what's your definition of *when that situation happens again in real life* you might need very large and deep trees to get a good estimate, let alone that to work well you have to have a good bounding algorithm, which is not trivial to obtain
  
  As [username_2](https://ai.stackexchange.com/users/1847/username_2), you probably also need to develop a very sophisticated and complex model to simulate your scenario
  
  > **Comments:**
  > **username_2**: For "real life", the OP would also need a rather sophisticated predictive model for exploring "every possible scenario".  
  
  ---
  
  