本文關鍵詞： 情感計算 智能教學系統(tǒng) 教學agent 情感建模 情感反饋策略　出處：《西南大學》2008年博士論文　論文類型：學位論文

【摘要】： 智能agent是人工智能非常重要的一個研究領域,因其自治性、主動性、社會性的特點得到廣泛應用。越來越多的智能教學系統(tǒng)開始采用智能agent作為構建體系結構的支撐技術,并由此形成了教學agent這一新的研究方向�，F(xiàn)代認知科學和神經生理科學的研究顯示情感對人類的認知具有重要的影響。而教育科學研究領域也從未停止過對學生學習過程中情感體驗的關注。因此,賦予教學agent情感處理的能力就成為該領域一個非常明確的研究目標。 受心理學關于情感模型研究的啟發(fā),智能agent研究領域也開始關注情感要素在智能系統(tǒng)建模中的作用,并陸續(xù)出現(xiàn)了一些智能agent的情感模型。然而,當前的大部分模型仍集中于agent的外部反應行為而很少考察其內部狀態(tài)。因此,當前的情感模型多是基于一些靜態(tài)規(guī)則和預先定義的領域知識生成情感,缺乏自適應性。同時,現(xiàn)有智能agent的情感模型對情感信息的處理還是基于二值邏輯,沒有體現(xiàn)出情感本身的動態(tài)性和模糊性的本質。 另一方面,本文針對教學agent的比較研究發(fā)現(xiàn),盡管現(xiàn)有一些設計已嘗試利用學生的情感狀態(tài)去選擇更適合學生的教學方法,卻鮮有激發(fā)和調動學生的積極情感從而對學生形成情感支持的研究。此外,現(xiàn)有的教學agent即使已嘗試情感建模,但僅停留在增強可信性的層次上,尚未真正賦予教學agent情感識別的能力和情感合成機制。 本文提出了基于模糊邏輯的教學agent情感建模方法,體現(xiàn)了情感動態(tài)型和模糊性的本質;使用機器學習算法實現(xiàn)了對教學agent情感經驗信息的學習,增強了模型的自適應性。在此基礎上,本研究設計了一個能夠識別學生情感,具有情感表現(xiàn)能力,能為學生在學習過程中提供情感支持,并最終促進學生學習效果的教學agent——情感教學agent(Emotional and Pedagogical Agent,EPA)。論文的主要工作和創(chuàng)新點包括: (1)提出了基于模糊邏輯和機器學習的情感建模方法。 使用模糊集合表征事件對目的影響程度,目的重要程度和期望程度,并探索了基于模糊邏輯規(guī)則的情感評估方法,以相對少的模糊規(guī)則實現(xiàn)在可觀測事件和情感狀態(tài)上的平滑轉換,體現(xiàn)了人類情感動態(tài)性和模糊性的特點;使用機器學習算法實現(xiàn)了教學agent對事件預期值等經驗信息的學習,增強了教學agent情感模型的自適應性。 (2)提出了基于學生動機類型的情感識別方法。 有關學生學習動機的研究顯示,學生的學習動機類型(表現(xiàn)取向,掌握取向)對其學習中可能的情感狀態(tài)產生重要影響。本研究在情感識別方法中引入學生的動機類型,體現(xiàn)了學生情感識別過程中的個體差異。 (3)設計了旨在對學生形成情感支持的情感教學agent反饋策略。 心理學和教育學領域的研究顯示,學生的消極情感會對其學習形成障礙,而積極情感則會促進學生的學習。本研究在識別學生情感狀態(tài),獲知其動機類型的基礎上,為情感教學agent設計了一系列情感反饋策略及其決策機制,通過情感行為和話語信息觸發(fā)學生的積極情感,消除消極情感的影響,實現(xiàn)對學生的情感支持。
[Abstract]:Agent intelligent artificial intelligence is a very important research field, because of its autonomy, initiative, social characteristics have been widely used. More and more intelligent tutoring systems begin to adopt intelligent agent as the key technology of the architectures, and thus the formation of the agent teaching as a new research direction of modern cognitive science and. Neurophysiology study shows have important influence on human cognition and emotion. And the field of education and science research has never stopped learning emotion experience in the process of attention to students. Therefore, given the ability of teaching agent emotional processing becomes a very clear research objective in this field.
Psychology about inspired emotional model research, research in the field of smart agent also began to pay attention to the emotional elements in the intelligent system modeling function, and there are some intelligent agent emotion model. However, for most of the current external reaction model is still focused on agent and rarely investigated its internal state. Therefore, the current model of emotion many are some static rules and predefined domain knowledge generation based on emotion, lack of self adaptability. At the same time, the existing processing emotion model of intelligent agent of emotional information is based on two valued logic, no emotion itself reflects the dynamic and fuzzy nature.
On the other hand, compared to research on Teaching of agent this paper, although some existing design has attempted to use the students' emotional state to choose more suitable teaching methods for students, positive emotion and arouse students rarely stimulate to form of emotional support for the students. In addition, the existing teaching agent even have attempted modeling of emotions, but only stay in the enhanced credibility level, has not really given teaching agent emotion recognition ability and emotion synthesis mechanism.
This paper puts forward the teaching agent Emotion Modeling Method Based on fuzzy logic, essence of the emotional dynamic type and fuzziness; using machine learning algorithm of agent teaching experience information learning, enhance the adaptability of the model. On this basis, this study designed a can recognize students' emotions, with emotion performance, provide emotional support for students in the learning process, and ultimately promote the learning effect of students teaching agent (Emotional and Pedagogical agent teaching Agent, EPA). The main work and innovation points include:
(1) an affective modeling method based on fuzzy logic and machine learning is proposed.
The degree of using fuzzy set representations of events to influence, important degree and degree of expectation, and to explore the emotional evaluation method based on fuzzy logic rules with fuzzy rules can be implemented in relatively few smooth observations and emotional change in state, embodies the human emotion dynamic and fuzzy characteristics; machine learning algorithm to achieve the value of information on agent learning experience events expected, adaptability of teaching agent emotion model.
(2) a method of emotion recognition based on the type of student motivation is proposed.
Study on students' learning motivation, learning motivation types (performance orientation, mastery oriented) have an important impact on the possible emotional state during their learning. This study introduces students in recognition of the types of motivation, reflects the students' individual differences in emotion recognition process.
(3) a agent feedback strategy is designed to create emotional support for students.
The researches in psychology and pedagogy, students' negative emotions are hindrance to their learning, and positive emotion can improve the students' learning. The study of emotion in recognition of student status, based on the known types of motivation, emotion teaching agent designed a series of emotional feedback strategies and decision-making mechanism, actively trigger the feelings of the students through the emotional behavior and discourse information, to eliminate the influence of negative emotions, the students realize the emotional support.

【學位授予單位】：西南大學
【學位級別】：博士
【學位授予年份】：2008
【分類號】：G420

【引證文獻】

相關期刊論文 前1條

1 趙慧勤;孫波;;虛擬教師情感合成模型的研究[J];中國電化教育;2012年01期

相關碩士學位論文 前1條

1 王磊;基于智能體的配色知識學習系統(tǒng)的研究[D];吉林大學;2012年

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本文編號：1543167