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INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.4 Issue 2, Pg.: 24-28 February 2016 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 ARTIFICIAL INTELLIGENCE EDUCATION: EMOTIONAL COMPUTATION Dr. Pranav Patil Assistant Professor, Department of Computer Science, M. J. College, Jalgaon, Maharashtra, India Abstract: - After decades of work towards making AI, some researchers are currently trying to form machines that are showing emotion intelligent. The quality definition of AI is that the ability for a machine to “think or act humanly or rationally”. The push towards showing emotion intelligent machines is an exciting addition to the current field. I will be able to give a survey of topics and resources for teaching emotional computation in AI courses. I will be able to show that this subject area is a wonderful application of recent AI techniques, and is a crucial side of recent analysis in AI. The knowledge base nature of work during this part isn't only compelling inside the classroom, it's going to conjointly result in wider interest within the field of computing. Keywords: Artificial Intelligence, Emotion, Student, Education life 1. Introduction Many researchers within the fields of AI and Human computer Interaction have predictable that the machines or intelligent agents of the extended run should connect on an emotional level with their users. This is supported the notion that an intelligent and winning human isn't only robust in mathematical, verbal and logical reasoning, however is in a position to attach with other folks. A lot of recent add this space has targeted on empowering agents with the flexibility to each observe feeling via verbal, non-verbal, and matter cues, and also express feelings through speech and gesture. During this article, I will present proof of this movement towards systems with emotional intelligence whereas also showing why and the way this subject should be enclosed in introductory AI courses. 2. Emotional Intelligence The construct of Emotional Intelligence became outstanding within the late 1980’s; but, Thorndike mentioned an identical construct known as social intelligence much earlier, in 1920. Emotional intelligence deals specifically with one’s ability to understand, understand, manage, and express feelings among one self and in managing others. The 5 domains important to emotional intelligence: knowing one’s emotions, managing emotions, motivating oneself, recognizing emotions in others, and usage relationships. A typical exist of Emotional Intelligence is emotional IQ, as Dr. Pranav Patil Page 24 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.4 Issue 2, Pg.: 24-28 February 2016 gauged by a myriad of wide revealed this tests. Within the late 1990s, several AI and Human computer Interaction researchers began to need the concept of emotion and emotional intelligence rather seriously. The theories and applications of machines that are showing emotion intelligent, it's necessary to first perceive that feeling is a very important side of intelligence. The proof that necessitates this work comes from some completely different fields. I will offer references to a number of this proof before getting in the work that has been completed in building machines that are showing emotion intelligent. The common notion is that feeling could be a hindrance to intelligent thought. A lot of add the sector of emotional neuroscience has provided empirical proof that this is often not the case, and so the alternative is true. Emotional neurobiology is that the study of the process of emotions among the human brain. Researchers during this field have shown that feeling plays an important role in drawback finding and different psychological feature tasks among the brain additionally to the empirical proof that arises from emotional neuroscience, several publications from fashionable scientific discipline have backed this notion similarly, difference of opinion that emotional intelligence is important to a person’s success in several aspects of life. 3. Models of emotion Prior to understanding efforts during this area, one should have an understanding of the varied models of feeling that are incorporated into systems. the choice of model is totally addicted to the task at hand; specifically what dimensions of emotion are often gleaned from the obtainable signalling, what model lends itself best to internal reasoning at intervals a system, and what sort of emotional expression the system aims to carry out. the best model is one among valence (positive or negative) and intensity, wherever sentiment is pictured as one score between -1 and +1, wherever -1 denotes the foremost intense negativity and +1 corresponds to the foremost intense positive score. A rather a lot of advanced model adds the dimension of dominance (a scale from submissive to dominant). During this model, the intensity dimension is termed “arousal” (a scale from calm to excited). This a lot of advanced model is usually called the VAD model, that stands for valence, arousal, and dominance This model is usually used in menstruation emotional attitude in humans as these extent lend themselves well to the present task. A lot of usually known model is Ekman’s 6 emotions model – gladness, sorrow, irritation, fear, surprise and disgust. This six dimensional model is meant to characterize emotional facial expressions and is typically used in systems that shall express feelings in deal with users. A mapping between the above models exists to help building systems that each discover and express feelings. 3.1. Technology with Emotional Intelligence With a traditional want for add this area, I will currently present the ways that during which researchers are creating efforts towards building showing emotion intelligent machines. There are varied models/definitions of emotional intelligence, however all of them boil all the way down to the power to attach with others by detection, expressing, managing and understanding the emotions of one self and others. Efforts in building machines that are showing emotion intelligent focus on a some key efforts: empowering the machine to notice emotion, enabling the machine to specific emotion, and at last, embodying the machine during a virtual or physical approach. Comes that incorporate all of those aspects additionally need the extra ability to handle and keep up an emotional interaction with a user, an oversized adscititious complexness. Within the sections that follow, I will offer samples of systems that approach these tasks – examples that are meant to show students to figure within the area of emotional intelligence. 3.2. Detecting emotion Work in automatic approaches to detective work feeling has centered on many alternative inputs together with verbal cues, non-verbal cues together with gestures and facial expressions, bodily signals like skin conduction similarly as matter information. the top goal in building systems that are ready to find an emotional reply from a user, is to understand that reply and act so – a problem that's larger, and fewer understood than the matter of simply detective work the emotional expression within the 1st place. The system, decision Reasoning Through Search, uses a machine learning move toward for the assignment of classifying a given piece of text inside the intensity model of Dr. Pranav Patil Page 25 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.4 Issue 2, Pg.: 24-28 February 2016 emotion. Different similar systems are designed supported the opposite models of emotion mentioned within the previous section – but, this technique was restricted to the apparently certainty of ranking a range of text on the size from -1 to +1 (extremely negative to extraordinarily positive). The system is trained on 106,000 picture show and products reviews, wherever star ratings (from one to 5) served as truth knowledge. Given the task at hand, a strictly applied math approach performs poorly owing to the disparities between the emotional connotations of some words between domains/contexts. as an example, once describing a political candidate, the term “cold” usually features a negative connotation, since its positive for a drinkable to be “cold.” to create a “general” (not domain specific) emotional classifier, a case primarily based approach is a lot of acceptable to avoid the results of averaging. The Reasoning through Search system combines the advantages of the Naïve Bayes Model similarly as Case primarily based Reasoning and varied techniques from info Retrieval. The top system classifies an unseen review on the valence/intensity model with 79% accuracy. I have found the Reasoning through Search system to be an attractive example to use in teaching machine learning via Naïve Thomas Bayes and Case based mostly reasoning. The task of emotional classification is straightforward enough that students simply perceive the challenges, however advanced enough to let students to explore the tradeoffs of varied machine learning approaches. By providing students with a training set of moving-picture show and products reviews, they are ready build a Naïve Thomas Bayes classifier and learn the shortcomings of the model initial hand among a medium-sized programming assignment. 3.3. Expressing emotion Emotional expression inside computer systems is usually targeted on applications involving speech and/or facial expressions/gestures. Again, a inordinateness of work exists in this space, all of which might have interaction students during a room setting, together with systems that commit to change gestures and expressions for an avatar and people that enhance emotional expression through pc generated speech To introduce students to the current thought, my add the latter is a decent example of machines that express emotion. This work is given during a digital theatre installation referred to as Buzz. In building a team of digital actors for Buzz, I wished to allow them to convey feeling in their computer generated voices. Normal text-to-speech engines are rather flat, emotionless; they wouldn’t bring a compelling performance. I selected to enhance an off-the shelf text-to-speech engine with a layer of feeling expression This layer was well-read by Cahn’s work outlining the acoustic parameters of speech that modification with totally different emotional states the matter of once to precise emotion is speech was resolved by an easy mood classifier (trained on a collection of structure of mind labeled web log posts. The actual transformations of the speech were done by an audio post giving out tool that altered the audio file of language supported the proof from Cahn’s work. The top result is a system that real conveys emotion (reliable with the content of what they are saying) in its voice. 4. Embodiment Finally, the embodiment of a system facilitates a a lot of personal connection between machine and user. Individuals typically attribute different human characteristics to a system once it perceives it as somewhat human/animal wanting. This not only ends up in emotional connections, however it makes users a lot of forgiving once the system makes miscalculation. Several on-line systems in e-commerce, tutoring and coaching applications have recently begun embodiment as the simplest way to engage/connect with users. Whereas there are several example systems during this area starting from robotic seals to game primarily based avatars, I realize the foremost compelling example to be destiny an automaton created within the android artificial intelligence cluster at MIT. The explanation destiny is like nice example is that it's an embodied system that detects, manages and expresses feeling in an exceedingly social interaction with a personality's. Whereas this is often not the only system during this area, I believe it is a compelling example to introduce students to the state of the art all told aspects of emotional intelligence. Dr. Pranav Patil Page 26 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.4 Issue 2, Pg.: 24-28 February 2016 4.1. Why educate emotional intelligence? AI may be a field that's young and perpetually dynamic. So as for students receive an education that allows them to create future contributions in AI, it is my opinion that Introductory AI courses got to balance historical work with this state of issues and approaches within the field of AI. Making machines with emotional intelligence is rising as a distinguished space in AI, and that I feel that students may gain advantage from an introduction to the current topic among the scope of an introductory AI course. To boot, the tasks concerned in making showing emotion intelligent machines lend themselves well to AI techniques generally enclosed in introductory courses. Additionally to being an interesting topic, building machines with emotional intelligence may be a task that's inherently multidisciplinary. several have hypothesized that multidisciplinary work will have wider charm, brining girls and different underrepresented teams into the sector of computing “In explicit, some studies show that students from underrepresented teams in computing, like women and minority students, who show early interest in computing are a lot of seemingly to pursue career methods in computing after they are exposed to process applications that demonstrate familiar relatedness”. 4.2. Current AI courses In this paper, I actually have provided a survey of topics and resources on the rising topic of building machines with emotional intelligence. in addition, I actually have provided reasoning on why this subject is very important and will be enclosed in introductory AI courses, however I actually have however to deal with the problem of the way to incorporate this subject within the framework of existing computer science courses. Through my experiences in two totally different courses, I actually have explored two strategies of introducing this subject. the primary technique involves exploring this subject towards the end of associate introductory AI course. The equally necessary within the philosophical foundations of the sphere and may be extended to debate efforts in building machines with emotional intelligence. The second technique, that I actually have found more compelling, is to introduce the subject of emotional intelligence on the early day of course group, once “essential AI.” during the semester, I tie in examples among the range of AI techniques I’m teaching. as an example, once teaching Machine Learning, we tend to discuss ways to classify the emotional content of text; once teaching speech generation, I present Janet Cahn’s work on emotional speech and show these findings were utilized in kismat to create an additional emotional voice once wrapping up the chapters on agent primarily based work, we tend to discuss the embodiment of agents and therefore the effects/reactions from users. 5. Conclusion The design of machines that are allowed with emotional intelligence could be an analysis space that's growing at intervals the sector of AI. I actually have provided a survey of topics and references of analysis toward this finish. My experiences introducing this subject in artificial intelligence courses are positive and that I have found it to be an interesting and necessary space of study. REFERENCES: [1] Breazeal, C. (2000), “Sociable Machines: Expressive Social Exchange between Humans and Robots". Sc.D. dissertation, Department of Electrical Engineering and Computer Science, MIT. [2] Breazeal (Ferrell), C. And Velasquez, J. (1998), “Toward Teaching a Robot `Infant’ using Emotive Communication Acts". In Proceedings of 1998 Simulation of Adaptive Behaviour, workshop on Socially Situated Intelligence, Zurich Switzerland. [3] Cañamero, D. (2001) “Emotions and adaptation in autonomous agents: a design perspective”. Cybernetics and systems: international journal. V. 32, 2001. [4] Cahn, J. E. The Generation of Affect in Synthesized Speech. Journal of the American Voice I/O Society, 1990. [5] Damasio A.R. Descartes' Error: Emotion, Reason, and the Human Brain. Grosset/Putnam, New York: 1994. Dr. Pranav Patil Page 27 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.4 Issue 2, Pg.: 24-28 February 2016 [6] Liu, H., Lieberman, H., and Selker, T. A model of textual affect sensing using real-world knowledge. In Proceedings of the 8th international conference on Intelligent user interfaces, 2003. [7] Margolis, J., Fisher, A., Unlocking the Clubhouse: Women in Computing. MIT Press, 2002. [8] Mayer, J.D. & Salovey, P. 1993. The intelligence of emotional intelligence. Intelligence. [9] Mehrabian, A. Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in Temperament. Current Psychology, Vol. 14, No. 4. [10] B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of EMNLP. [11] Picard, R.W. Affective Computing. MIT Press, Cambridge, 1997. [12] Polzin, T., and Waibel, A. Detecting Emotions in Speech. In Proceedings of the CMC, 1998. [13] Russell, S.J., Norvig, P. Artificial Intelligence: A Modern Approach. Prentice Hall, New Jersey: 1995. [14] Salovey, P. & Mayer, J.D. 1990. Emotional intelligence. Imagination, Cognition, and Personality. [15] Sood, Sara Owsley. Compelling Computation: Strategies for Mining the Interesting. PhD Thesis, 2007. [16] Thorndike, E.L. 1920. Intelligence and its use. Harper's Magazine. [17] Tjaden, Brian. A multidisciplinary course in computational biology. Journal of Computing Sciences in Colleges, June 2007. [18] Turney, P.D., and Littman, M.L... Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems. Dr. Pranav Patil Page 28