【摘要】：隨著科學(xué)技術(shù)的發(fā)展,近年來,人們對人工智能的研究熱情可謂如火如荼。在許多領(lǐng)域取得了重大突破,例如:google的AlphaGo就是著名一例,機器戰(zhàn)勝了人類,是人工智能領(lǐng)域的一個新標桿,具有劃時代的意義。人工智能在語音識別、人臉識別、自動駕駛、智能搜索、博弈、定理證明等領(lǐng)域得到了廣泛應(yīng)用。在教育教學(xué)方面,也陸續(xù)出現(xiàn)各種教學(xué)教輔平臺,迄今為止,市場上還沒有真正意義上的智能產(chǎn)品,能夠像人一樣的解答問題,并給出解題步驟。本文正是在這個背景下,研究和構(gòu)建了基于規(guī)則流的認知模型,并將其應(yīng)用于平面解析幾何問題求解,設(shè)計和實現(xiàn)了一個類人智能答題系統(tǒng),更好地為智能教育教學(xué)提供服務(wù)。本文主要研究內(nèi)容包括以下幾個部分:(1)初等數(shù)學(xué)概念與關(guān)系的知識表示。計算機進行初等數(shù)學(xué)解題的前提是必須能夠理解其中的概念。本文將這些概念抽象為“實體”和“關(guān)系”,然后為這些實體和關(guān)系表示成一階謂詞邏輯的形式。這樣,就可以把一個題目的已知條件和結(jié)論進行自動轉(zhuǎn)換,然后系統(tǒng)就可基于這些已知條件及結(jié)論進行解題。(2)公理和定理等規(guī)則的知識表示。要想計算機能夠進行類人答題,其每一步計算或推理都必須遵循相應(yīng)的數(shù)學(xué)邏輯,而這些數(shù)學(xué)邏輯就是初等數(shù)學(xué)中的公理、定義、定理和推論等。本文將每一個公理、定理等表示成相應(yīng)的產(chǎn)生式規(guī)則。(3)基于規(guī)則流的認知模型的自動構(gòu)建。規(guī)則庫中的規(guī)則在匹配執(zhí)行的時候,是亂序的,不確定的,如果我們把執(zhí)行頻率比較高的規(guī)則提取出來,經(jīng)過一定的拼接整理,形成認知模型鏈,存儲在認知模型庫中,供下次解題使用,那么系統(tǒng)的推理就具有了方向性和目的性。(4)基于認知模型的解析幾何問題求解系統(tǒng)的設(shè)計與實現(xiàn)。由于認知模型使推理具有了目的性,減少了無效規(guī)則的匹配,所以基于認知模型的解析幾何求解系統(tǒng)會在效率上有所提高�；跇�(gòu)建的認知推理模型,設(shè)計和實現(xiàn)了一個解析幾何問題求解系統(tǒng),可進行自動類人答題。通過大量的實驗驗證,系統(tǒng)的問題求解效率得到了較大提高,問題求解準確率可達60%。
[Abstract]:With the development of science and technology, in recent years, people's enthusiasm for artificial intelligence research can be said to be in full swing. Great breakthroughs have been made in many fields, for example: google's AlphaGo is a famous example, the machine defeated the human being, is a new benchmark in the field of artificial intelligence, has the epoch-making significance. Artificial intelligence is widely used in speech recognition, face recognition, autopilot, intelligent search, game, theorem proving and so on. In the aspect of education and teaching, a variety of teaching-assisted platforms have appeared one after another. Up to now, there are no real intelligent products in the market, which can solve problems like people and give the steps to solve the problems. Under this background, this paper studies and constructs a cognitive model based on rule flow, and applies it to solving plane analytic geometry problems, designs and implements a human-like intelligent problem answering system, and provides better services for intelligent education and teaching. The main contents of this paper are as follows: (1) knowledge representation of elementary mathematical concepts and relationships. The premise of computer solving elementary mathematics problem is that it must be able to understand its concept. In this paper, these concepts are abstracted as "entity" and "relation", and then expressed in the form of first-order predicate logic for these entities and relationships. In this way, the known conditions and conclusions of a problem can be automatically transformed, and then the system can solve the problem based on these known conditions and conclusions. (2) the knowledge representation of axioms and theorems and other rules. In order for a computer to be able to answer a class of questions, each step of its calculation or reasoning must follow the corresponding mathematical logic, which is axioms, definitions, theorems and corollaries in elementary mathematics. In this paper, each axiom, theorem and so on are expressed as corresponding production rules. (3) automatic construction of cognitive model based on rule flow. The rules in the rule base are random and uncertain when they are matched and executed. If we extract the rules with high frequency of execution and sort them together to form a cognitive model chain, we can store them in the cognitive model library. For the next time, the reasoning of the system has the directivity and purpose. (4) the design and implementation of the analytic geometry problem solving system based on cognitive model. Because the cognitive model makes reasoning purposeful and reduces the matching of invalid rules, the analytic geometric solution system based on cognitive model can improve the efficiency of the system. Based on the cognitive reasoning model, an analytic geometry problem solving system is designed and implemented. Through a large number of experiments, the problem-solving efficiency of the system is greatly improved, and the accuracy of the problem-solving is up to 60%.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O182

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