Answers.Body: <p>Thinking about this more, the answer is in fact yes, but not for the application you mention.</p>
  
  <p>You cannot use alpha-beta pruning to learn a model to predict customer outcomes, because it is only useful for domains where you are concerned about an <em>adversary</em>. In finding a customer model, there is no reason to worry about someone coming in and forcing you to make bad decisions about the optimization of the model. Consequentially, there is no reason to use minimax search, and thus, to use alpha-beta pruning.</p>
  
  <p>There <em>are</em> applications other than (video) games where you could use these techniques though. For example, there are <a href="http://teamcore.usc.edu/" rel="nofollow noreferrer">security games</a>. In these "games" we want to use AI to find a strategy to protect an airport. It is reasonable to try and design our model under the assumption that someone else wants to break it. You could use Alpha-Beta pruning here (although in practice, more sophisticated algorithms are used). </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-10-24T17:14:59.460Z
Answers.Text: So, just confirming. It wouldn't be wrong trying to solve a problem like finding right customer for a product using minimax/alpha-beta pruning the way I explained it?
Answers.UserDisplayName: kritika
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Answers.CreationDate: 2019-10-24T17:36:12.190Z
Answers.Text: @kritka It would be a little unusual, but there are ways to do it.
Answers.UserDisplayName: John Doucette
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Answers.CreationDate: 2019-10-24T17:37:00.070Z
Answers.Text: @kritka although the way you described sounds like decision tree induction, which is something else all together.
Answers.UserDisplayName: John Doucette
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-10-24T17:37:26.770Z
Answers.Text: Just curious, what would be the ways?
Answers.UserDisplayName: kritika
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-10-24T17:38:49.400Z
Answers.Text: Yes, I was thinking of comparing algorithms like decision tree induction, heuristic search, minimax search for the same problem l described.
Answers.UserDisplayName: kritika
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Answers.CreationDate: 2019-10-24T17:05:20.237Z
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Answers.LastEditDate: 2019-10-24T19:40:21.443Z
Answers.UserDisplayName: John Doucette
Body: <p>Can alpha-beta pruning/ minimax be used for systems apart from games? Like for selecting the right customer for a product, etc. (the typical data science problems)? I have seen people do it, but can't understand how. Can someone help me understand that?</p>
  
  <p>Can I do something like if - find two criteria on which customers can buy product depends on like gender and age. Find the probability for all the customers depending on age and gender if they can buy it.</p>
  
  <p>like if there are 3 customers - there probability to buy a product on the basis of their age and gender is - Customer 1 - (20%, 30%), Customer 2 - (30%, 60%), Customer 3 - (40%, 20%). here the x and y represents - (probability based on age, probability based on gender ). Probability is probability to buy the product.</p>
  
  <p>For minimax, will it be correct if one player(max) tries to select the customer on basis of gender and other player(min) on basis of age. so, one can be max and one can be min. </p>
  
  <p>Dont know if this correct or not, but just a idea.</p>
  
Tags: <applications><data-science><minimax><alpha-beta-pruning>
Title: Can alpha-beta pruning be used for applications apart from games?
UserDisplayName: kritika
ThreadText: # Can alpha-beta pruning be used for applications apart from games?
  
  (Asked by: username_0 on 2019-10-24)
  
  Can alpha-beta pruning/ minimax be used for systems apart from games? Like for selecting the right customer for a product, etc. (the typical data science problems)? I have seen people do it, but can't understand how. Can someone help me understand that?
  
  Can I do something like if - find two criteria on which customers can buy product depends on like gender and age. Find the probability for all the customers depending on age and gender if they can buy it.
  
  like if there are 3 customers - there probability to buy a product on the basis of their age and gender is - Customer 1 - (20%, 30%), Customer 2 - (30%, 60%), Customer 3 - (40%, 20%). here the x and y represents - (probability based on age, probability based on gender ). Probability is probability to buy the product.
  
  For minimax, will it be correct if one player(max) tries to select the customer on basis of gender and other player(min) on basis of age. so, one can be max and one can be min.
  
  Dont know if this correct or not, but just a idea.
  
  **Tags:** applications, data-science, minimax, alpha-beta-pruning
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-10-24. Score: 1 | Accepted answer
  
  Thinking about this more, the answer is in fact yes, but not for the application you mention.
  
  You cannot use alpha-beta pruning to learn a model to predict customer outcomes, because it is only useful for domains where you are concerned about an *adversary*. In finding a customer model, there is no reason to worry about someone coming in and forcing you to make bad decisions about the optimization of the model. Consequentially, there is no reason to use minimax search, and thus, to use alpha-beta pruning.
  
  There *are* applications other than (video) games where you could use these techniques though. For example, there are [security games](http://teamcore.usc.edu/). In these "games" we want to use AI to find a strategy to protect an airport. It is reasonable to try and design our model under the assumption that someone else wants to break it. You could use Alpha-Beta pruning here (although in practice, more sophisticated algorithms are used).
  
  > **Comments:**
  > **username_0**: So, just confirming. It wouldn't be wrong trying to solve a problem like finding right customer for a product using minimax/alpha-beta pruning the way I explained it?  
  > **username_1**: @kritka It would be a little unusual, but there are ways to do it.  
  > **username_1**: @kritka although the way you described sounds like decision tree induction, which is something else all together.  
  > **username_0**: Just curious, what would be the ways?  
  > **username_0**: Yes, I was thinking of comparing algorithms like decision tree induction, heuristic search, minimax search for the same problem l described.  
  
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