Research on Advanced Systems of Efficient Hypothesis Finding

高效假设发现的先进系统研究

• 批准号: 17300051
• 负责人: INOUE Katsumi
• 金额: $ 7.8万
• 依托单位: National Institute of Informatics
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2005
• 资助国家: 日本
• 起止时间: 2005 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17300051/
• 关键词: consequence finding; hypothesis finding; SOL resolution; knowledge discovery; induction; abduction; inductive logic programming; nonmonotonic reasoning; 人工知能; アルゴリズム; CF 帰納法

In this research, we developed new advanced methods for hypothesis finding and implemented efficient hypothesis-finding systems based on consequence-finding techniques. The proposed systems, SOLAR and CF-induction, compute abductive and inductive hypotheses, respectively. Research results can be summarized as the following three items.1. Basic theories for advanced hypothesis-finding (Iwanuma, Inoue, Nabeshima).We developed theoretical results on equivalence of abductive theories, generality of default theories and logic programs, and induction of causal laws in action descriptions. We also studied some aspects of circumscriptive induction, which is a new integrated framework of explanatory induction and descriptive induction, and proposed a new first-order approximation method, called pointwise circumscriptive induction, which enables us to derive some interesting hypotheses by the ordinary consequence finding calculus.2. Development of an efficient consequence-finding system (Nabeshi … More ma, Iwanuma, Inoue).We showed that a size-preserving upside-down transformation of any SOL/Connection tableau can be achieved by the folding-up operation, and gave a size-optimal consequence finding calculus for an incrementally axiom-increasing environment. We also developed a consequence-finding method with non-stable production fields and applied it to complete abduction from full clausal theories. We further implemented a new version of the consequence-finding system SOLAR with various state-of-the-art pruning techniques. Handling axioms with equality is also investigated in the SOLAR framework, and a prototype system of a C++ version of SOLAR is firstly realized.3. Hypothesis-finding by CF-induction and its application (Inoue).CF-induction is a sound and complete inductive logic programming system to compute hypotheses from full-clausal theories.CF-induction consists of several nondeterministic procedures, and in particular its generalization procedure is assumed to be any complete combination of inductive operators. We thus proposed a method to reduce possible combinations of generalization operators by preserving the soundness and completeness of CF-induction. Next, we applied CF-induction to estimation of possible enzymatic reaction states in metabolic pathways. In this work, we showed that CF-induction can compute not only possible states of enzymatic reactions but also causal relations that are missing in the current background theory. Less

在这项研究中，我们开发了新的先进的假设发现方法，并实现了有效的假设发现系统的基础上，结果发现技术。建议的系统，太阳能和CF-感应，计算溯及假设和归纳，分别。研究成果可以概括为以下三项.高级假设发现的基本理论（岩沼、井上、锅岛）。我们开发了关于溯因理论的等价性、缺省理论和逻辑程序的通用性以及动作描述中因果律的归纳的理论结果。我们还研究了一种新的解释归纳和描述归纳的综合框架--限制归纳的某些方面，并提出了一种新的一阶近似方法，称为逐点限制归纳，它使我们能够通过普通的结论发现演算导出一些有趣的假设.开发有效的后果调查系统（Nabeshi ...更多信息 ma，Iwanuma，Inoue）.我们证明了任何SOL/Connection表的大小保持倒置变换都可以通过折叠操作实现，并给出了增量公理增长环境下的大小最优结果发现演算.我们还开发了一个结果发现方法与非稳定的生产领域，并将其应用于完整的子句理论的完整溯因。我们进一步实现了一个新版本的后果发现系统SOLAR与各种国家的最先进的修剪技术。在SOLAR框架下研究了等式公理的处理，并首次实现了SOLAR的C++版本的原型系统. CF-归纳法的假设发现及其应用（Inoue）. CF-归纳法是一个完善的归纳逻辑程序设计系统，用于从全子句理论中计算假设. CF-归纳法由几个非确定性过程组成，特别是它的推广过程被假设为归纳算子的任何完整组合.因此，我们提出了一种方法，以减少可能的组合推广运营商保持健全性和完整性的CF-归纳。接下来，我们应用CF-诱导代谢途径中可能的酶反应状态的估计。在这项工作中，我们表明CF-诱导不仅可以计算酶促反应的可能状态，而且可以计算当前背景理论中缺少的因果关系。少

项目成果

Towards a Logical Reconstruction of CF-Induction.

走向 CF 归纳的逻辑重建。

• 发表时间: 2008
• 期刊: New Frontiers in Artificial Intelligence, Lecture Notes in Artificial Intelligence 4914
• 作者: 宇藤 陽介;南角 吉彦;李 晃伸;徳田 恵一;Yoshitaka Yamamoto
• 通讯作者: Yoshitaka Yamamoto

Consequence Finding and Computing Answers with Defaults

使用默认值查找结果并计算答案

• 发表时间: 2006
• 期刊: Journal of Intelligent Information Systems 26(1)
• 作者: 小林 隆二;篠田 浩一;古井 貞煕;Katsumi Inoue
• 通讯作者: Katsumi Inoue

Estimation of Possible Reaction States in Metabolic Pathways using Inductive Logic Programming.

使用归纳逻辑编程估计代谢途径中可能的反应状态。

• 发表时间: 2008
• 作者: 広瀬啓吉 編著;徳田恵一 分担;徳田恵一;Yoshitaka Yamamoto
• 通讯作者: Yoshitaka Yamamoto

Knowledge-based Discovery in Systems Biology using CF-Induction.

使用 CF-Induction 在系统生物学中进行基于知识的发现。

• 发表时间: 2007
• 期刊: New Trends in Applied Artificial Intelligence, Lecture Notes in Artificial Intelligence 4570
• 作者: 全 柄河;南角 吉彦;徳田 恵一;Andrei Doncescu
• 通讯作者: Andrei Doncescu

情報量と頻度に基づく系列データマイニングにおける非同期パターンの抽出と高速化

基于信息量和频率的顺序数据挖掘中异步模式的提取和加速

• 发表时间: 2008
• 作者: Nguyen Huu Bach;篠田 浩一;古井 貞煕;村田順平
• 通讯作者: 村田順平