GGP with Advanced Reasoning and Board Knowledge Discovery
GGP with Advanced Reasoning and Board Knowledge Discovery
复制标题
具有高级推理和董事会知识发现功能的 GGP
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
A. Lancucki
中科院分区：
文献类型：
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作者：
A. Lancucki
一般游戏玩法（GGP）的质量与状态转换缓慢和知识模块相比。实例化和命题网络可在基于序言的推理上获得巨大的性能，但不能很好地扩展。在此出版物中，MGDL（剥离功能常数的GDL的变体）被定义为简单推理机器的基础。 MGDL允许将规则轻松映射到C ++功能。在270个经过测试的GDL规则表中，有253个符合MGDL而没有进行任何修改；其余的需要微小的更改。对C ++翻译方案进行了修订（M）GDL；已重新评估；与YAP Prolog相比，它的收益从28％到7300％不等，设法在几秒钟内编译了苛刻的规则表。为了增强游戏知识，已经提出了受到计算机GO的类似成功技术启发的空间功能。因为他们需要欧几里得指标，因此通过一组地面原子句定义了GDL的小董事会扩展。基于SGA的遗传算法是为了调整游戏参数和进行自我玩法而设计的，因此可以从有意义的游戏记录中挖掘出功能。该方法已在一个小集群上进行了测试，使性能获得了高达20％的胜利。拟议思想的实现构成了GGP Spatium的核心 - 一个小型的C ++/Python GGP框架，用于开发紧凑的GGP播放器和问题解决者。
Quality of General Game Playing (GGP) matches suffers from slow state-switching and weak knowledge modules. Instantiation and Propositional Networks offer great performance gains over Prolog-based reasoning, but do not scale well. In this publication mGDL, a variant of GDL stripped of function constants, has been defined as a basis for simple reasoning machines. mGDL allows to easily map rules to C++ functions. 253 out of 270 tested GDL rule sheets conformed to mGDL without any modifications; the rest required minor changes. A revised (m)GDL to C++ translation scheme has been reevaluated; it brought gains ranging from 28% to 7300% over YAP Prolog, managing to compile even demanding rule sheets under few seconds. For strengthening game knowledge, spatial features inspired by similar successful techniques from computer Go have been proposed. For they required an Euclidean metric, a small board extension to GDL has been defined through a set of ground atomic sentences. An SGA-based genetic algorithm has been designed for tweaking game parameters and conducting self-plays, so the features could be mined from meaningful game records. The approach has been tested on a small cluster, giving performance gains up to 20% more wins against the baseline UCT player. Implementations of proposed ideas constitutes the core of GGP Spatium - a small C++/Python GGP framework, created for developing compact GGP Players and problem solvers.
亚马逊的蒙特卡洛方法
DOI：
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发表时间：
2007
期刊：
Proceedings of Computer Games Workshop
影响因子：
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作者：
J. Kloetzer;H. Iida;B. Bouzy
通讯作者：
B. Bouzy