Acquisition of Evaluation Function of high precision by Learning

通过学习获得高精度评价函数

• 批准号: 15500084
• 负责人: KAWAI Satoru
• 金额: $ 2.5万
• 依托单位: The University of Tokyo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2003
• 资助国家: 日本
• 起止时间: 2003 至 2004
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15500084/
• 关键词: evaluation functions; dynamic evaluator; knowledge acquisition; state transition rules; Galois lattice; reflective calculation; non-wellfounded set; aspect; feature; evaluation functions; dynamic evaluator; knowledge acquisition; state transition rules; Galois lattice; reflective calculation; non-wellfounded set; aspect; feature

The main and final target of this research is the automatic construction of evaluators by learning, integrating state evaluation by learning, concept formation, and creation of efficient evaluators. Main results of the component researches follow.1.Automatic construction of evaluation functionsThe evaluation functions are constructed, without human preparation, only from the basic rules and framework of the target field. The essential part of this construction is the automatic creation of simple data sets for state evaluation, followed by the selection based on the appropriateness of the data. Following some preparatory studies carried out in 2001 and 2002, more precise evaluation functions are obtained by the analysis of bulk data for states evaluation.2.Concept formation and structure analysisThe structure of a Galois lattice, based on a partial order defined among the set of simple piecewise statuses, is studied as a framework of concepts. In this research, semistructured data, frequently found in the real world, is also studied. The "summary structure" can be extracted from such "non-well ordered structure" by an analytical method.3.Reflection and aspectsIn the target system of this research, a lot of software agents for state evaluation are to be prepared by the method of reflection. These agents are expected to act optimally in the real evaluation referring the concept database. In this research, the framework of aspect oriented programming is studied for interclass function increments. Some results in support systems and theoretical framework in aspect oriented programming are obtained.

这项研究的主要目标和最终目标是通过学习，概念形成和有效评估者的创建来自动构建评估者，整合国家评估。 1.评估功能的自动构建功能的主要结果。评估功能仅根据目标场的基本规则和框架来构建，而没有人类准备。该构建的基本部分是自动创建简单数据集以进行状态评估，然后根据数据的适当性进行选择。经过2001年和2002年进行的一些准备研究，通过分析批量评估的大量数据获得了更精确的评估功能。2。概念形成和结构分析基于简单分段状态的部分定义的Galois晶格的结构，研究为概念框架。在这项研究中，还研究了在现实世界中经常发现的半结构化数据。可以通过分析方法从这种“非井有序结构”中提取“摘要结构”。3。反射和本研究的目标系统，许多用于状态评估的软件代理应通过反思方法来制备。预计这些代理将在参考概念数据库的实际评估中最佳起作用。在这项研究中，研究了面向方面的编程的框架以增加类间功能的增量。获得了一些在面向方面的编程中支持系统和理论框架的结果。