Knowledge representation and reasoning method based on relation database 一種基于關(guān)系數(shù)據(jù)庫(kù)的知識(shí)表示和推理方法
During the study on the realization of computer - aided conceptual design system of mechanism design , the thesis utilized some knowledge and technology in the artificial intelligence field such as knowledge representation and reasoning 在對(duì)計(jì)算機(jī)輔助機(jī)構(gòu)概念設(shè)計(jì)系統(tǒng)的實(shí)現(xiàn)的研究中,論文利用了人工智能領(lǐng)域中的知識(shí)表示和推理等知識(shí)與技術(shù)。
16 rendell d a , cui z , cohn a g . a spatial logic based on regions and connection . international conference on knowledge representation and reasoning , 1992 , pp . 165 - 176 . 17 fiadeiro jos e l . categories for software engineering 進(jìn)一步把帶類(lèi)型范疇的概念推廣為類(lèi)范疇,可以描述不完全和有缺陷的知識(shí),以及不完全知識(shí)和有缺陷知識(shí)消除缺陷和完備化的過(guò)程。
This paper describes the details about knowledge representation and reasoning based on uncertainty in ai , and the outline of belief network . after introducing the causality diagram model and summarizing conventional reasoning algorithm , a new reasoning approach of causality diagram has been presented , which is aimed at the defects in conventional reasoning algorithm , which are the large amount of boolean computation and its complexity 論文詳細(xì)地介紹了人工智能中不確定性知識(shí)表達(dá)及其推理的有關(guān)內(nèi)容,并簡(jiǎn)要介紹了信度網(wǎng)知識(shí)表達(dá)方式;在介紹因果圖知識(shí)表達(dá)模型、總結(jié)單值因果圖的常規(guī)推理算法后,針對(duì)單值因果圖常規(guī)推理算法中存在邏輯運(yùn)算量大、計(jì)算復(fù)雜的困難,根據(jù)早期不交化的思想,提出了一種單值因果圖推理的新方法。
This dissertation discusses and studies to surround the knowledge representation , learning , reasoning , and the main contents include : at the first chapter , some familiar uncertain knowledge representation and reasoning and the difficulties of them : evidential theory , certainty factor , fuzzy logic and fuzzy reasoning , subjective bayesian method , belief network are introduced . we present the basic knowledge , primary reasoning algorithm , complexity of reasoning algorithm , the way of dealing with some problem of causality diagram relative and the research direction in causality diagram theory particular at the second chapter 論文圍繞著因果圖的知識(shí)表達(dá)、學(xué)習(xí)、推理進(jìn)行了討論和研究,主要內(nèi)容包括:在扼要介紹了一些比較常見(jiàn)的不確定性知識(shí)的表示和推理方法:證據(jù)理論、確定性因子、模糊邏輯與模糊推理、主觀bayes方法、信度網(wǎng)的基本知識(shí)之后,比較詳細(xì)地闡述了因果圖的知識(shí)表達(dá),主要的推理算法、計(jì)算復(fù)雜度以及對(duì)一些問(wèn)題的處理方式方法。
百科解釋
Knowledge representation (KR) is an area of artificial intelligence research aimed at representing knowledge in symbols to facilitate inferencing from those knowledge elements, creating new elements of knowledge. The KR can be made to be independent of the underlying knowledge model or knowledge base system (KBS) such as a semantic network.