Issue 5 implement artificial intelligence algorithms #24
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This includes the AIPlayer and AIOutcome enums, helper methods for building from the game's state, and unit tests. See issue #5.
The AIOpponent struct contains the majority of the AI algorithms logic. Test and documentation are added to describe how to use the structure and to ensure it works as expected. Note: There are still some rough spots in this commit such as duplicated code for finding the best position and insufficient documentation examples. See issue #5.
Rename the AIOpponent and AIOutcome structures to Opponent and Outcome respectively. Since these live in the AI module the AI prefix is unnecessary. See issue #5.
The worst case AI update time is where a new game with an empty board must be evaluated. See issue #5.
Refactor the ai::best_position() and ai::worst_outcome() functions to use a loop instead of a series of if-else statements. This reduces code duplication with the set / map key names and simplifies the functions. See issue #5.
The updated documentation includes a full game played by two AI opponents.
This commit adds additional benchmarks: one for playing an entire game and others for the perfect AI with various numbers of remaining free positions. This helps provide more insight on the performance characteristics of the library.
The specification for ai::Opponent::new() is to clamp the mistake probability to the range [0, 1]. This was not being done which would have resulted in a panic for incorrect values provided. This commit ensures the values are being clamped.
j-richey
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Adding the AI algorithms per the task described in task #5.
Note: the
README.mdfile is not yet updated to mention how to run the benchmarks. I felt like this will be better if done at the same time as adding the code example and other updates that are schedule as part of task #6.