Description: GUI using java prepared Riversi games, search algorithms using classical game tree, and on this basis have done a lot of optimization, the evaluation function I used Simon M. Lucas and Thomas P. Runarsson published in its Temporal Difference Learning Versus Co-Evolution for Acquiring Othello Position Evaluation in the difference by comparing the real-time learning (TDL, Temporal Difference Learning) and co-evolution (CEL, Co-Evolution) Smileys for the calculation of the valuation function to be one of the best of the WPC , the valuation function at the same time including the calculation of mobility. At the same time, a viable location for the calculation, I used the method based on the look-up table. For start, I used the Kyung-Joong Kim and Sung-Bae Cho s Evolutionary Othello Players Boosted by Opening Knowledge use of 99 kinds of well-defined start of the table. Generally speaking, AI very smart, the default level, however most people are under it.
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audio
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Othello.class
OthelloPlayer.class
Othello.java
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