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Status of Implementation


• The implementation of the AI checker player is complete


• My player wins against the Naive_AI when played as both white and black


• It is yet unable to beat the Strong_AI





• The AI has a problem of sometimes falling into a loop of moves, where it keeps doing the same moves again and again. I tried to resolve the problem however wasn’t successful. It does the looping only with the Naïve_AI because the Naïve_AI also falls into a loop.


Description of the Board Evaluation Function used


For designing the board evaluation function I have taken ideas from the following sites


• http//ai-depot.com/LogicGames/AlphaBeta-Cutoff.html


• CHECKERS By Roee Gutman and Tamir Rosenblum


The evaluation function has been basically designed as a weighted sum of properties such as


• Piece Advantage - difference in the number of pieces


• King Advantage - difference in the number of Kings


• Position Advantage - weighted sum of the board positions occupied


• Move Advantage - total number of possible moves


• Back Row - guard back row till possible


• Men to King - encourage men to become kings


• No of Captures - no of opposite player piece we can captured


• Pieces Captured - number of our pieces that can be captured





the evaluation function uses two sets of weights, the first set is used during the initial phase of the games when number of total pieces is greater then 1 and the next set of weights are used when we have 1 or less pieces on the board. This is done to indicate change in priorities.


Evaluation of the AI player


• The AI falls intermediate between Naïve_AI and Strong_AI


• I feel if we can manage a right mix of weights my AI player can beat the strong _AI


• There scope to implement learning, by making the AI learn the weights instead of us hard coding the weights.





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