臺灣博碩士論文加值系統

近年來越來越多寵物機器人 (Pet Robot) 的產品出現，顯示寵物機器人逐步進展成為下一代電子玩具，而研究指出產品的創新性有助於提高消費者的接受度，未來寵物機器人將具備更多的變化性與趣味性，於是在智慧型機器人的研究中，如何使機器人能與人類在同一環境下共同活動，融入人類的生活而不顯得冰冷生硬，已成為學者們努力實現的目標。事實上，所有的傳統機器人產品都需要面對兩大問題，一是使用者只有短暫的熱情，在熟悉系統後便會覺得無趣而不再使用，二是傳統機器人之行為完全受控於指令操作，如此直接的操作行為模式使得使用者無法脫離正在與機器人互動的想法。本研究為了使機器人表現的更加自然，設計以寵物機器人為對象之簡化系統原型，加入制約模型 (Conditional Model) 使得每個寵物機器人擁有獨特的互動模式，其中，制約模型之學習機制以古典制約 (Classical Conditioning) 為理論基礎，計算模型則使用聯想神經網路 (Associative Neural Network) 與霍伯學習法則 (Hebbian Learning Rule)，可實現習得 (Acquisition)、遺忘 (Extinction)、再習得效應 (Reacquisition Effects) 等基本特性，以及阻斷 (Blocking) 和部份的二級制約 (Secondary Conditioning) 等延伸特性。情緒方面，本系統加入情緒模型 (Emotional Model)，仿照實際生物的內分泌與情緒間的影響關係，組成八種基本情緒，同時亦利用情緒 (Emotion) 及感受 (Feeling) 的概念，採用情緒驅動的方式來決定最後的表現行為，並於文內展示兩個方面的成果，第一為制約學習影響寵物機器人之情緒，第二為情緒影響寵物機器人之行為。期望它如同生物般學習、像生物般地表現自我，令使用者可從與寵物機器人的互動中感受到多一點生命性，獲得更自然與信賴的體驗，藉以改善傳統機器人之兩大問題。

Recently more and more pet robot products are launched, which showing the increasing market demand for pet robots, and a plenty of researches have pointed out that the product innovativeness can encourage consumers’ acceptance, the pet robot will be made with more variability, more versatility, and more interesting in each generation. To make the robots do common activities in the same environment with human and integrate it to human’ life is the target of those researchers who were studying in the field of intelligent robot. As a matter of fact, all traditional robots have to face with two major issues: one is the user limitation of patience and passion, and the other is the users cannot escape from the thought of “interacting with a hard and cold robot machine”. This research is aimed at making the pet robots perform more naturally; therefore a simplified prototype system has been designed, it is composed of conditional model and emotional model. The conditional model can make every pet robot have unique interactive style, its theoretical foundation of learning method is based on classical conditioning. And the computational model is binding up with associative neural network and Hebbian learning rule, it can implements acquisition, extinction, and reacquisition effects as basic characteristics, and other extensive characteristics, such as blocking and secondary conditioning. On the other hand, the emotional model was modeled on the impact of the actual biological relationship between endocrine and emotion, which was built up eight basic emotions; apart from this, the concepts of emotion and feeling have also been adopted, thus determine the final performance behavior by using mood-driven approach. In this research, two aspects of achievement will be demonstrated: the first is how the learning method affects the emotion of pet robot, and the second is how the emotion affects the behavior of pet robot. Hopefully, the pet robot can learn like a human, performing biological self-expression, making people think the pet robot is animated machine, and people will get more trusting experience while they are interacting with each other.

【目錄】
摘要 I
Abstract II
目錄 III
圖目錄 IV
表目錄 VI
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 1
第三節 論文架構 2
第二章 文獻探討 3
第一節 古典制約 3
第二節 情緒模型 4
第三節 類神經網路 12
第三章 系統分析 15
第一節 制約模型 15
第二節 情緒模型 38
第四章 系統設計 42
第一節 系統概觀 42
第二節 模組設計 44
第五章 成果展示 50
第一節 寵物機器人外型 50
第二節 寵物機器人互動 50
第六章 結論 54
參考文獻 55


【圖目錄】
圖 1 古典制約刺激反應關係 4
圖 2 OCC情緒模型分類與階層關係圖 6
圖 3 Wundt情感三維理論圖 7
圖 4 Plutchik因素分析法圖 8
圖 5 Russell al.環形情感模型圖 9
圖 6 Kismet 機器人 9
圖 7 Breazeal al. 情感空間圖 10
圖 8 EFA性格空間的構造方法圖 11
圖 9 生物之神經元模型 (Source: Retrieved July 20, 2014, from http://www.icr.org/article/thinking-about-brain/) 12
圖 10 神經元模型 13
圖 11 制約學習機制－習得 16
圖 12制約學習機制－遺忘 17
圖 13 測試再習得效應流程圖 18
圖 14 簡化神經網路制約圖 20
圖 15 不帶衰減項使權重呈線性成長 (γ = 0) 21
圖 16 帶衰減項使抑制權重成長 (γ = 0.1) 21
圖 17 未制約前非制約刺激與制約刺激出現狀態軌跡 24
圖 18 未制約前反應狀態軌跡 24
圖 19 未制約前權重更新軌跡 25
圖 20 習得之非制約刺激與制約刺激出現狀態軌跡 26
圖 21 習得之反應狀態軌跡 26
圖 22 習得之權重更新軌跡 27
圖 23 遺忘之非制約刺激與制約刺激出現狀態軌跡 28
圖 24 遺忘之反應狀態軌跡 28
圖 25 遺忘之權重更新軌跡 29
圖 26 再習得效應之非制約刺激與制約刺激出現狀態軌跡 30
圖 27 再習得效應之反應狀態軌跡 30
圖 28 再習得效應之權重更新軌跡 31
圖 29 阻斷之非制約刺激與制約刺激出現狀態軌跡 32
圖 30 阻斷之反應狀態軌跡 32
圖 31 阻斷之權重更新軌跡 33
圖 32制約抑制之非制約刺激與制約刺激出現狀態軌跡 34
圖 33 制約抑制之反應狀態軌跡 34
圖 34 制約抑制之權重更新軌跡 35
圖 35 二級制約之非制約刺激與制約刺激出現狀態軌跡 36
圖 36 二級制約之反應狀態軌跡 36
圖 37 二級制約之權重更新軌跡 37
圖 38 內分泌與基本情緒關係立方圖 39
圖 39 情緒、感受關係圖 40
圖 40 寵物機器人系統架構 43
圖 41 寵物機器人系統流程 44
圖 42 基於類神經網路建構古典制約機制 45
圖 43 情緒模組方塊圖 46
圖 44 行為控制器架構 47
圖 45 動作及聲音與內分泌之配對關係 47
圖 46 速度、形狀與音頻意義 48
圖 47 寵物機器人 50
圖 48 制約學習前各刺激之反應 52
圖 49 (左)握左手與摸頭建立關聯 (右)握左手、握右手與摸頭發生阻斷 52
圖 50 (左)握右手與拍背建立關聯 (右)握右手與摸頭建立關聯 52
圖 51 握右手發生遺忘 53
圖 52 握右手與摸頭發生再習得效應 53
圖 53 表現行為隨時間變化 53

【表目錄】
表 1 獎懲因子特性 23
表 2 制約特性之計算模型比較表 38
表 3 內分泌與基本情緒對應表 39
表 4 刺激強度表 46
表 5 情緒對應表情 48

【英文文獻】
[1]Official website for Pleo pet robot. http://www.pleoworld.com/
[2]Official website for Sony AIBO pet robot. http://www.sonyaibo.co.uk/
[3]Official website for Robi robot. http://robi.tw/
[4]Nakashima, T., Fukutome, G., & Ishii, N. (2010, August). Healing Effects of Pet Robots at an Elderly-Care Facility. In Computer and Information Science (ICIS), 2010 IEEE/ACIS 9th International Conference on (pp. 407-412). IEEE.
[5]Huang, S. H. (2010). Fluency Evaluation Aided by Mandarin Chinese Syntax for A Reading Assistant Robot (Master dissertation, National Taiwan University Department of Engineering Science and Ocean Engineering).
[6]Liu, Y. C. (2010). Improving Oral Reading Rhythm by a Pet-like Robot (Master dissertation, National Taiwan University Department of Engineering Science and Ocean Engineering)
[7]Shibata, T., Tashima, T., & Tanie, K. (1999). Emergence of emotional behavior through physical interaction between human and robot. In Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on (Vol. 4, pp. 2868-2873). IEEE.
[8]Lövheim, H. (2012). A new three-dimensional model for emotions and monoamine neurotransmitters. Medical hypotheses, 78(2), 341-348.
[9]http://en.wikipedia.org/wiki/L%C3%B6vheim_cube_of_emotion, 2014
[10]Kubota, N., Nojima, Y., Baba, N., Kojima, F., & Fukuda, T. (2000). Evolving pet robot with emotional model. In Evolutionary Computation, 2000. Proceedings of the 2000 Congress on (Vol. 2, pp. 1231-1237). IEEE.
[11]Ming-Hsiang Su. (2003). A User-Oriented System for Developing Emotion-Based Pet Robots (Master dissertation, National Pingtung University of Science and Technology Institute of Information Management).
[12]Jee, E. S., Park, S. Y., Kim, C. H., & Kobayashi, H. (2009, September). Composition of musical sound to express robot's emotion with intensity and synchronized expression with robot's behavior. In Robot and Human Interactive Communication, 2009. RO-MAN 2009. The 18th IEEE International Symposium on (pp. 369-374). IEEE.
[13]Dang, T. H. H., Hutzler, G., & Hoppenot, P. (2011, June). Mobile robot emotion expression with motion based on MACE-GRACE model. In Advanced Robotics (ICAR), 2011 15th International Conference on (pp. 137-142). IEEE.
[14]Tsung-Yen Chang. (2006). A Research of Expression Design for Intelligent Agent (Master dissertation. National Taiwan University of Arts Department of Multimedia and Animation Arts).
[15]Heng-Yao Liang. (2013). Applying Stimulus-Response Matrix to Design Scriptable Intelligent Doll (Master dissertation, Chung Yuan Christian University Department of Information and Computer Engineering).
[16]Tomkins, S. (1963). Affect Imagery Consciousness: Volume II: The Negative Affects. Springer Publishing Company.
[17]I-Tsen Liu. (2012). The Role of Emotion between Emotional and Symbolic Expression (Department of Visual Communication Design Shu-Te University).
[18]Arbib, M. A., & Fellous, J. M. (2004). Emotions: from brain to robot. Trends in cognitive sciences, 8(12), 554-561.
[19]Sanz, R., Hernández, C., Hernando, A., Gómez, J., & Bermejo, J. (2009). Grounding robot autonomy in emotion and self-awareness. In Advances in Robotics (pp. 23-43). Springer Berlin Heidelberg.
[20]Chi-Tai Cheng., Yu-Ting Yang., Shih-Heng Miao., Ching-Chang Wong. (2009). Motion and Emotional Behavior Design for Pet Robot Dog. Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics (pp. 13-22).
[21]Kubota, N., Kojima, F., & Fukuda, T. (2001, July). Self-consciousness and emotion for a pet robot with structured intelligence. In IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th (pp. 2786-2791). IEEE.
[22]Ting-Ting Chen. (2013). Designing a motion script of 3D software agents (Master dissertation, Chung Yuan Christian University Department of Information and Computer Engineering).
[23]Johansson, C., & Lansner, A. (2002). An associative neural network model of classical conditioning. Dept. Numerical Analysis and Computing Science, KTH,(TRITA-NA-P0217).
[24]Hebb, D. O. (2002). The organization of behavior: A neuropsychological theory. Psychology Press.
[25]Balkenius, C., & Morén, J. (1998, September). Computational models of classical conditioning: a comparative study. In From animals to animats 5: proceedings of the fifth international conference on simulation of adaptive behavior (pp. 348-353). MIT Press/Bradford Books: Cambridge, MA.
[26]Pavlov, I. P. (1927). Conditioned reflexes. Oxford: Oxford University Press.
[27]Shibata, T., & Tanie, K. (1999). Creation of subjective value through physical interaction between human and machine. In Proceeding of the 4th International Symposium on Artifcial Life and Robotics (pp. 20-23).
[28]James, W. (2011). The principles of psychology. Digireads. com Publishing.
[29]Nakajima, H. U. Y. H. H. (1998, September). Emotion model for life-like agent and its evaluation. In AAAI 98 (p. 62). Aaai Pr.
[30]Kamin, L. J. (1968). Attention-like” processes in classical conditioning. In Miami symposium on the prediction of behavior: Aversive stimulation (pp. 9-31).
[31]Balkenius, C. (1995). Natural intelligence in artificial creatures (Vol. 37). Lund University.
[32]Minsky, M. (1988). Society of mind. Simon and Schuster.
[33]Franklin, S. (1997). Artificial minds. MIT press.
[34]Russell, S., Norvig, P., & Intelligence, A. (1995). A modern approach. Artificial Intelligence. Prentice-Hall, Egnlewood Cliffs, 25.
[35]Hassenzahl, M. (2004). Emotions can be quite ephemeral; we cannot design them. interactions, 11(5), 46-48.
[36]Brave, S., & Nass, C. (2002). Emotion in human-computer interaction. The human-computer interaction handbook: fundamentals, evolving technologies and emerging applications, 81-96.
[37]Mehrabian, A. (1968). Communication without words. Psychological today, 2, 53-55.
[38]Ekman, P., & Friesen, W. V. (1967). Head and body cues in the judgment of emotion: A reformulation. Perceptual and motor skills, 24(3), 711-724.
[39]Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386.
[40]Minsky, M., & Papert, S. (1969). Perceptron: an introduction to computational geometry. The MIT Press, Cambridge, expanded edition, 19, 88.
[41]Kohonen, T. (1972). Correlation matrix memories. Computers, IEEE Transactions on, 100(4), 353-359.
[42]Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.
[43]McClelland, J. L., Rumelhart, D. E., & PDP Research Group. (1986). Parallel distributed processing. Explorations in the microstructure of cognition, 2.
[44]Official website for Geminoid-F robot. http://www.geminoid.jp/
[45]Official website for IDA robot. http://www.conscious-robots.com/
[46]Picard, R. (1997). Affective computation.
[47]Fox, E. (2008). Emotion science cognitive and neuroscientific approaches to understanding human emotions. Palgrave Macmillan.
[48]Gaulin, S. J., & McBurney, D. H. (2001). Psychology: An evolutionary approach. Prentice Hall.
[49]Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3-4), 169-200.
[50]Gomi, T., Vardalas, J., & Ide, K. I. (1995, July). Elements of artificial emotion. In Robot and Human Communication, 1995. RO-MAN'95 TOKYO, Proceedings., 4th IEEE International Workshop on (pp. 265-268). IEEE.
[51]Kort, B., Reilly, R., & Picard, R. W. (2001, August). An affective model of interplay between emotions and learning: Reengineering educational pedagogy-building a learning companion. In Advanced Learning Technologies, IEEE International Conference on (pp. 0043-0043). IEEE Computer Society.
[52]Picard, R. W., Vyzas, E., & Healey, J. (2001). Toward machine emotional intelligence: Analysis of affective physiological state. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 23(10), 1175-1191.
[53]Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.
[54]Frijda, N. H., Kuipers, P., & Ter Schure, E. (1989). Relations among emotion, appraisal, and emotional action readiness. Journal of personality and social psychology, 57(2), 212.
[55]Ortony, A. (1990). The cognitive structure of emotions. Cambridge university press.
[56]Wundt, W. (1980). Outlines of psychology (pp. 179-195). Springer US.
[57]Plutchik, R. (1980). Emotion: A psychoevolutionary synthesis (p. 440). New York: Harper & Row.
[58]Russell, S. J., & Subramanian, D. (1995). Provably bounded-optimal agents. arXiv preprint cs/9505103.
[59]Breazeal, C., & Scassellati, B. (1999). How to build robots that make friends and influence people. In Intelligent Robots and Systems, 1999. IROS'99. Proceedings. 1999 IEEE/RSJ International Conference on (Vol. 2, pp. 858-863). IEEE.
[60]Roseman, I. J., Spindel, M. S., & Jose, P. E. (1990). Appraisals of emotion-eliciting events: Testing a theory of discrete emotions. Journal of Personality and Social Psychology, 59(5), 899.
[61]Wilson, I. (2000). The artificial emotion engine, driving emotional behavior. In AAAI spring symposium on artificial intelligence and interactive entertainment (pp. 20-22).
[62]Sorabji, R., & Aristote. (1972). Aristotle on memory (p. 48). London: Duckworth.
[63]Berkeley, G. (1878). A treatise concerning the principles of human knowledge. JB Lippincott & Company.
[64]Hume, D. (1988). An enquiry concerning human understanding (Vol. 1). Avalon Travel.
[65]Warren, H. C. (1921). A history of the association psychology. Charles Scribner's Sons.
[66]Mill, J. (1869). Analysis of the Phenomena of the Human Mind (Vol. 1). Longmans, Green, Reader and Dyer.
[67]Klopf, A. H. (1988). A neuronal model of classical conditioning. Psychobiology, 16(2), 85-125.
[68]Balkenius, C., & Morén, J. (1999). Dynamics of a classical conditioning model. Autonomous robots, 7(1), 41-56.
[69]Adler, G. (2002). Psychology: The science of behaviour. Canadian Psychology, 43(4), 278.
[70]Huang, C. C. Applying Emotion Model to Design Learning Companion Agent. University ofZhongyuan, master paper.


【中文文獻】
[71]蘇明祥. (2003). 使用者導向之情感寵物機器人設計系統，國立屏東科技大學資訊管理系, 碩士論文.
[72]周書暉, & 林祐全. (2011). 結合情境與情緒: 人機互動理論沿革與發展. 傳播與管理研究, 11(1), 29-68.
[73]毛峽, (2011). 人機情感交互. 科學出版社.
[74]葉怡成, & 土木工程. (1993). 類神經網路模式應用與實作. 儒林出版.
[75]林逸塵. (2002). 類神經網路應用於空氣品質預測之研究. 高雄: 國立中山大學環境工程研究所, 碩士論文.
[76]廖凡宇. (2010). 以類神經網路在股價預測之研究. 虎尾科技大學資訊管理研究所學位論文, 1-43.
[77]林逸塵. (2002). 類神經網路應用於空氣品質預測之研究. 高雄: 國立中山大學環境工程研究所, 碩士論文.
[78]王念中, & 郭宗益. (1999). 應用類神經網路專家系統診斷電力變壓器故障之研究.
[79]高仲仁. (2001). 運用類神經網路進行隧道岩體分類 國立中央大學 應用地質研究所碩士論文, 共 87 頁.
[80]虞曉, 胡光銳, & 徐雄. (2000). 採用聚類神經網絡與分離輸出語音重構的語音分離算法. 上海交通大學學報, 34(6), 748-751.
[81]黃庭影. (2007). 改良式雙向聯想記憶類神經網路加解密之研究. 臺灣師範大學機電科技研究所學位論文, 1-95.
[82]Lu, T. C. (2013). 應用類神經網路於生產排程之搬運時間預測. 成功大學航空太空工程學系學位論文.
[83]胡玉成. (1992). 暢談類神經網路.倚天出版
[84]張春興, & 心理學. (1991). 現代心理學: 現代人研究自身問題的科學. 臺灣東華.