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.
- [java_Othello] - wrote reversi Java games, the Internet v
- [Othello] - Riversi write their own source, artifici
- [Othello] - Machine learning artificial intelligence
- [othello] - othello.m A simple othello program Auth
File list (Check if you may need any files):
061221015
.........\代码
.........\....\Generator.rar
.........\....\Othello.rar
.........\....\readme.txt
.........\文档
.........\....\Othello设计文档.doc
.........\....\Othello设计文档.pdf
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