Answers.Body: <p>According to comments below, this reply seems to be incorrect. See the comments for more links and discussion.</p>
  <p>Link to Maven: <a href="https://en.wikipedia.org/wiki/Maven_(Scrabble)" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Maven_(Scrabble)</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-12-21T11:10:42.990Z
Answers.Text: The term "solved" has a very specific meaning in the context of game AI, which definitely doesn't apply here. Even though it's already quite old, as far as I'm aware the PhD thesis about Maven is probably indeed still one of the more important source on AI for Scrabble. The full thesis can still be downloaded from this link: https://project.dke.maastrichtuniversity.nl/games/files/phd/Shepard_thesis.pdf
Answers.UserDisplayName: Dennis Soemers
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-12-21T13:17:00.460Z
Answers.Text: From what I've seen, your resources are pointing to Maven which doesn't involve reinforcement learning!
Answers.UserDisplayName: Ibrahim Radwan
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-04-06T13:29:47.993Z
Answers.Text: Scrabble is not close to solved. No AI is favored against the best humans. See [here](https://medium.com/@14domino/scrabble-is-nowhere-close-to-a-solved-game-6628ec9f5ab0) for expert analysis.
Answers.UserDisplayName: dshin
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-12-21T02:43:26.837Z
Answers.LastActivityDate: 2024-04-07T14:10:06.147Z
Answers.LastEditDate: 2024-04-07T14:10:06.147Z
Answers.UserDisplayName: Konstantin Solomatov
Body: <p>I've been thinking if machine learning can be used to play the game Scrabble. My knowledge is limited in the ML field, thus I've seeking some pointers :) </p>
  
  <p>I want to know how could I possibly build a model that picks a move from all the given valid moves of the current game state, and then plays the move and wait for the delayed reward. The actions here aren't static actions, they are basically selecting move to maximize the final score.</p>
  
  <p>Is there any way to encode the valid moves and then use a model to pick those moves?</p>
  
  <p>I've also considered the genetic approach, but I think if I can represent my move with a set of features (score, consonantVowels ration, rack leave score, #blank tiles after the move, ...etc), training a neural network like this could take a long time.</p>
  
  <p>Another training related question, is it feasible to run the training on a GPU given that I will be waiting for a response (the new game state) from the opponent (e.g. Quackle) after every action?</p>
  
  <p>Thank you :)</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-11-26T00:54:29.023Z
Comments.Text: Why do you want to use machine learning for this problem ? Isn't a simple dictionary more efficient ? 
  I understand you want to use machine learning for delayed reward, but when I think about Scrabble, I don't really see it as a problem : since you will have random letter the next step, there is no point to "keep your letters". When I play Scrabble, I just try to maximize the score for the current turn, and there is very little point of trying to maximize score for next turns.
Comments.UserDisplayName: Astariul
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-11-26T21:13:29.327Z
Comments.Text: I think there're some features that could describe each move, more than just its board score. Some of those features are the rack leave score, the number of blank tiles it uses compared to the score it's going to give me, the number of hot spots it opens, ... etc. Therefore I need to somehow tune the parameters for these features.
Comments.UserDisplayName: Ibrahim Radwan
Tags: <machine-learning><reinforcement-learning><genetic-algorithms>
Title: Scrabble game using machine learning
UserDisplayName: Ibrahim Radwan
ThreadText: # Scrabble game using machine learning
  
  (Asked by: username_0 on 2018-11-24)
  
  I've been thinking if machine learning can be used to play the game Scrabble. My knowledge is limited in the ML field, thus I've seeking some pointers :)
  
  I want to know how could I possibly build a model that picks a move from all the given valid moves of the current game state, and then plays the move and wait for the delayed reward. The actions here aren't static actions, they are basically selecting move to maximize the final score.
  
  Is there any way to encode the valid moves and then use a model to pick those moves?
  
  I've also considered the genetic approach, but I think if I can represent my move with a set of features (score, consonantVowels ration, rack leave score, #blank tiles after the move, ...etc), training a neural network like this could take a long time.
  
  Another training related question, is it feasible to run the training on a GPU given that I will be waiting for a response (the new game state) from the opponent (e.g. Quackle) after every action?
  
  Thank you :)
  
  **Tags:** machine-learning, reinforcement-learning, genetic-algorithms
  
  > **Comments:**
  > **username_1**: Why do you want to use machine learning for this problem ? Isn't a simple dictionary more efficient ?  I understand you want to use machine learning for delayed reward, but when I think about Scrabble, I don't really see it as a problem : since you will have random letter the next step, there is no point to "keep your letters". When I play Scrabble, I just try to maximize the score for the current turn, and there is very little point of trying to maximize score for next turns.  
  > **username_0**: I think there're some features that could describe each move, more than just its board score. Some of those features are the rack leave score, the number of blank tiles it uses compared to the score it's going to give me, the number of hot spots it opens, ... etc. Therefore I need to somehow tune the parameters for these features.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2018-12-21. Score: 2
  
  According to comments below, this reply seems to be incorrect. See the comments for more links and discussion.
  
  Link to Maven: <https://en.wikipedia.org/wiki/Maven_(Scrabble)>
  
  > **Comments:**
  > **username_3**: The term "solved" has a very specific meaning in the context of game AI, which definitely doesn't apply here. Even though it's already quite old, as far as I'm aware the PhD thesis about Maven is probably indeed still one of the more important source on AI for Scrabble. The full thesis can still be downloaded from this link: https://project.dke.maastrichtuniversity.nl/games/files/phd/Shepard_thesis.pdf  
  > **username_0**: From what I've seen, your resources are pointing to Maven which doesn't involve reinforcement learning!  
  > **username_4**: Scrabble is not close to solved. No AI is favored against the best humans. See [here](https://medium.com/@14domino/scrabble-is-nowhere-close-to-a-solved-game-6628ec9f5ab0) for expert analysis.  
  
  ---
  
  