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Description: < 游戏中的人工智能>>,pdf电子书-<Game artificial intelligence>>, pdf e-book
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Size: 4707328 |
Author: 滕飞 |
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Description: 一个支持人机对战的五子棋游戏，但AI算法比较简陋。和大家分享-A supportive man-machine war of Gobang game, but relatively simple AI algorithm. And the U.S. share
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Size: 4125696 |
Author: 金波 |
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Description: 五子棋的实现
博弈，该文档叫你如何一步一步实现五子棋，可以说是一个相当不错的ai算法-Gobang the realization of the game, the document tell you how, step by step to achieve Gobang can be said to be a fairly good ai algorithm
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Size: 3072 |
Author: qu |
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Description: Othello平台
很好的黑白棋图形界面，可以导入AI。
该平台的特点
1、可以修改棋盘的大小，不仅仅是传统的8*8棋盘，而且可以在棋盘上设置障碍点。
2、导入AI功能，可以AI互相对战，也可以人机、人人对战。
3、可以导入棋盘，保存、导入棋局
4、可以设定时间空间限制，程序自动显示AI使用的内存大小，以及每方累计使用的时间。-Othello Reversi good platform graphical interface, you can import AI. The characteristics of the platform 1, can modify the size of the chessboard, not just the traditional 8* 8 chessboard, but can also set up obstacles in the chessboard point. 2, import AI feature that AI against each other can also be man-machine, all the war. 3, can be imported board, save, import game 4, you can set time and space constraints, the procedure automatically display AI use of memory size, and each side use the accumulated time.
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Size: 616448 |
Author: kingbirdji |
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Description: Ai Game Programming Wisdom
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Size: 39413760 |
Author: godlikeme |
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Description: 使用java编写的GUI的黑白棋游戏，搜索算法采用经典的博弈树，并在此基础上做了大量优化，我的评估函数采用了Simon M. Lucas 和 Thomas P. Runarsson 在其合作发表的 Temporal Difference Learning Versus Co-Evolution for Acquiring Othello Position Evaluation 中通过对比即时差分学习（TDL，Temporal Difference
Learning）和协同进化（CEL，Co-Evolution）对于计算黑白棋的估值函数时得到的一个最佳 WPC，估值函数同时包括行动力的计算。于此同时，对于计算可行位置，我采用了基于查表的方法。对于开局，我使用了 Kyung-Joong Kim 和 Sung-Bae Cho 的 Evolutionary Othello Players Boosted by Opening Knowledge 使用的 99 种 well-defined 的开局表。
总体来说，AI智能很强，默认等级一般人是下不过它的。-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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Size: 3481600 |
Author: da |
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Description: 使用java編寫的GUI的黑白棋遊戲-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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Size: 59392 |
Author: chowkwing |
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Description: 游戏F.E.A.R（极度恐慌）里运用的基于目标驱动的AI决策框架。即G.O.A.P框架-Game FEAR (extreme fear) in the use of AI-based and goal-driven decision-making framework. Framework that GOAP
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Size: 2377728 |
Author: 刘志勇 |
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Description: ★实验任务
正如你所知道的 s_sin 是一个贪玩的不得了的小 P 孩 QAQ，你也知道他最近很喜欢玩一
个叫做太阳帝国的原罪的策略游戏去年他已经和疯狂的 AI 交战了整整一年。而现在，战斗
的序幕又要拉开了。
在某个星球上，该星球由 n*m 个方格组成，每个方格中可能为 boss，s_sin，障碍，道
路，小怪。s_sin 想要去打爆 boss，假设他可以秒杀 boss，现在他的任务只需要到达 boss
所在的位置。如果 s_sin 想要到达某个方格，但方格中有小怪，那么必须打死小怪，才能到
达这个方格。假设 s_sin 只能向上、下、左、右移动一个格子，移动一步用时 1 个时间单位，
杀死小怪也用 1 个时间单位。假设 s_sin 很强壮，可以杀死所有小怪。
试计算 s_sin 到达 boss 位置至少需要多少时间。注意：障碍时不能通过的。
★数据输入
输入第一行为两个正整数 n，m (1 < =n，m< =100)， 表示该地图有 n 行 m 列。
接下来 n 行，每行 m 个字符：“.”代表道路 ， “a”代表 boss ， “r”代表 s_sin ，
“#”代表障碍，“x”代表小怪。-The experimental task
As you know s_sin is a fun not the small child P QAQ, you know he loves playing with a recently
A strategy game called the sins of a Solar Empire last year and he has mad at AI for a whole year. And now, the battle
Prelude to open.
On a planet, the planet by n*m squares which, as boss, s_sin, obstacle each square, road
The road, small strange. S_sin wants to hook boss, assuming he can Seckill boss, his task now only need to reach boss
Location. If s_sin wants to reach a square box, but have a little strange, so must kill mobs, to
Reach a square. Assume that s_sin only to the upper and lower, left, right movement of a lattice, move in 1 time units,
Kill mobs in 1 time units. Assume that s_sin is very strong, can kill all the mobs.
Calculate the s_sin arrives at the boss position at least how much time. Note: barrier to adoption.
Data input
The first input two positive integers n, m (=n < 1, m< =100), said the map with n M.
The next N lines, M char
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Size: 2048 |
Author: 王一丞 |
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