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Epistemic Programming System Based on Strong Relevant Logic

基于强关联逻辑的认知规划系统

基本信息

批准号：
11480079
负责人：
CHENG Jingde
金额：
$ 3.78万
依托单位：
Saitama University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2002
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-11480079/
关键词：
scientific discovery epistemic processes epistemic programming strong relevant logic relevant reasoning ampliative reasoning autonomous evolution automated theorem finding 自己計測

项目摘要

1. We proposed some significant fundamental observations and assumptions on scientific discovery processes and their automation. Based on the observations and assumptions, we proposed a strong relevant logic model of epistemic processes in scientific discovery.2. Based on our strong relevant logic model of epistemic processes in scientific discovery, we proposed a novel program paradigm, named 'Epistemic Programming,' which regards conditionals as the subject of computing, takes primary epistemic operations as basic operations of computing, and regards epistemic processes as the subject of programming.3. We pointed out why the classical mathematical logic and its various classical and non-classical conservative extensions are not suitable to automated theorem finding, and shows that strong relevant logic is a more hopeful candidate for the purpose.4. We developed a forward deduction system for general-purpose entailment calculus, named EnCal, which can serve as the forward reasoning engine in an epistemic programming system. We improved the efficiency of EnCal by parallel processing techniques.5. We proposed the notion of autonomous evolutionary information system and its architecture.6. We proposed the notion of anticipatory reasoning-reacting system and showed that temporal relevant logics, which are obtained by introducing temporal operators and related axiom schemata and inference rules into strong relevant logics, can be used to underlie anticipatory reasoning.7. We proposed a new approach to knowledge acquisition problem : automated knowledge acquisition by relevant reasoning based on strong relevant logic.

1.我们对科学发现过程及其自动化提出了一些重要的基本观察和假设。在观察和假设的基础上，我们提出了科学发现中认知过程的强相关逻辑模型。基于我们对科学发现中认知过程的强相关性逻辑模型，我们提出了一种新的程序范式--认知编程，它以条件句为计算主体，以初级认知运算为计算的基本运算，以认知过程为编程主体。指出了经典数理逻辑及其各种经典和非经典保守扩展不适合于自动发现定理的原因，并指出强关联逻辑更有希望实现这一目的。我们开发了一个通用的蕴涵演算正向演绎系统EnCal，它可以作为认知程序设计系统中的正向推理引擎。通过采用并行处理技术，提高了EnCal的效率。提出了自主进化信息系统的概念及其体系结构。提出了预期推理-反应系统的概念，并证明了通过在强相关逻辑中引入时态算子及相关公理模式和推理规则而得到的时态相关逻辑可以用来支持预期推理。我们提出了一种解决知识获取问题的新方法：基于强关联逻辑的关联推理自动获取知识。