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in AI and Deep Learning by (50.2k points)

I have been thinking of making an AI for a board game for a long time, and recently I've started to gather resources and algorithms. The game is non-random, and most of the time, there < 3 moves for a player, sometimes, there are >20 moves. I would like to store critical moves, or ambiguous moves so that the AI learns from its mistakes and will not make the same mistake the next time. Moves that surely win or lose need not be stored. So I actually have a sparse decision tree for the beginning of games. I would like to know how I should store this decision tree in a database? The database does not need to be SQL, and I do not know which database is suitable for this particular problem.

EDIT: Please do not tell me to parse the decision tree into memory, just imagine the game as complicated as chess.

1 Answer

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by (108k points)

You can simply use an actual graph database to store your data of the sparse tree.

Then you can store key/value pairs in a node and you can also store edges that connect nodes.

For more information regarding the Case for a Wide-Table Approach to Manage Sparse

Relational Data Sets, refer the following link:

It will tell you how you can store your sparse decision tree in a relational database.

If you want to learn more about Decision Tree then visit this Artificial Intelligence Course.

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