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Symbiotic Transformational Technology on the rise: Artificial Intelligence in Emotional Intelligence

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Abstract

Emotions and Intelligence are allied spectacles and hence veritable intelligent agents are modelled taking into account the emotional quotient. Research and development in artificial intelligence have imbibed emotional intelligence as an important focus theme to scan across real-life disciplines. Noteworthy key-ins have been incorporated to evolve fresh imminent across the sphere of emotional intelligence and algorithms which deploy intelligent software decisions. Agents who delve into teaching-learning process are very alluring for merging emotional facets in artificial intelligence. It is an incredible fact that Emotions play an imperative component in intelligent behaviour and persuade the human judgment-making process. This research insight article focuses on the synoptic sketch of the state-of-the-art research highlights in emotional intelligence with prominence towards strategic features such as Emotion detection, Emotional agents, Text emotion detection and Modeling the setting of artificial and autonomous task agents.
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Symbiotic Transformational Technology on
the rise: Articial Intelligence in Emotional
Intelligence
S. Balakrishnan J. Janet S. Sheeba Rani
Professor, Dept. of CS and Engg., Sri Krishna Principal, Sri Krishna College of Engg. and Associate Professor, Dept. of Electrical and
College of Engg. and Tech., Coimbatore, Technology, Coimbatore, Tamilnadu, India. Electronics Engg., Sri Krishna College of Engg.
Tamilnadu, India. and Tech., Coimbatore, Tamilnadu, India.
Emotions and Intelligence are allied spectacles and hence veritable intelligent agents are modelled
taking into account the emotional quotient. Research and development in artificial intelligence have
imbibed emotional intelligence as an important focus theme to scan across real-life disciplines.
Noteworthy key-ins have been incorporated to evolve fresh imminent across the sphere of emotional
intelligence and algorithms which deploy intelligent software decisions. Agents who delve into
teaching-learning process are very alluring for merging emotional facets in artificial intelligence. It is
an incredible fact that Emotions play an imperative component in intelligent behaviour and persuade
the human judgment-making process. This research insight article focuses on the synoptic sketch of
the state-of-the-art research highlights in emotional intelligence with prominence towards strategic
features such as Emotion detection, Emotional agents, Text emotion detection and Modeling the setting
of artificial and autonomous task agents.
1. Introduction
With the onset of research in social
intelligence by Thorndike, the concept
of Emotional Intelligence has evolved in
the 19th century [1]. Social intelligence is
as the “ability to understand and manage
other people, and to engage in adaptive
social interactions” [2]; emotional
intelligence is defined as the ability of
an individual to recognize, comprehend,
administer, and articulate emotion
contained by oneself and in relating with
others [3]. Salovey et. al has formulated
five critical realms of EI: emotional
awareness, emotional management,
self motivation, conceding emotions
in others and managing relationships.
Emotional Intelligence is evaluated
using EQ (emotional intelligence
quotient) as a common gauging factor
through standard EQ tests.
Since the 19th century, research
forum in artificial intelligence and
human-computer interaction have
considered the role and contribution of
emotions as a significant factor. Picard
shared a contextual outline for designing
machines incorporated with emotional
intelligence. Consequently, numerous
other researchers pertaining to this
domain have developed machines that
can deduce on emotions with additional
capabilities to perceive, manage,
comprehend and articulate emotions.
Further research progress is currently
developing to incorporate emotional
entities in the following areas:
Program the engine to perceive
emotions
Facilitate the engine to articulate
emotion
Exemplify the engine in a virtual or
physical manner
An Intelligent Agent (IA) [4] – [7]
is considered to be a software entity
located in an environment. IA can be
Autonomous;
respond to changes in the
COVER STORY
Handling
Relationships
Recognising
others emotions
Motivating
oneself
Managing
emotions
Knowing ones
emotions
EMOTIONAL INTELLIGENCE
DOMAINS
Fig. 1: Facets of Emotional Intelligence
15
CSI COMMUNICATIONS | JUNE 2019
Fig. 2: Configuration of an Emotional Agent
Input
Rational
Emotional
Reactive
Output
Appraisal
Emotions
Planning / Deliberative
layer
Reactive Layer
World Model
Planning
Knowledge
World Interface
Perceptual input Action Output
Appraisal
Affective
Sensing Affective
Expression
Fig. 3: Emotional Agents as Hybrid Structure
environment;
be proactive in attaining its goals;
and also
Sociable.
For the purpose of attaining the
goal, an IA learns by itself and makes use
of its internal knowledge base. Thus it is
seen as a natural metaphor for human
acts. It has elevated performance
behaviour in data distribution and
control of self-imposed expertise.
There are five categories in the
Intelligent Agent-based systems:-
Integration: Integration of
information and sharing of
knowledge.
Coordination: Collaborative
problem-solving and poly-agent
structure.
Mobility: Mobile driving forces and
object oriented keys.
Assistance: Private assistance,
soft-bots and data mining.
Believable Agents: Alife and
simulation.
2. Emotion Detection
The mood and perception towards
the choice of desired brands or products
in the world of digital marketing which
reflect the mood of the consumer are
gauged by different organisations using
Sentiment Analysis. But when the
users engage with offline shopping of
brands and products in retail outlets,
showrooms, etc., the task to gauge
consumer mood and user reaction
becomes a challenging task to solve.
Hence Emotion Detection from facial
expressions using AI can be a feasible
substitutive option to automatically
gauge consumer’s rendezvous with
their desired choice of substance and
trade names.
3. Emotional Agents
An emotional agent is a driving
force that networks with its setting
based on an evenhanded assessment
that the environmental position has on
the aim, principles and inclusive point
of interests of that agent which is being
influenced.
3.1 Different Aspects of considering
Emotional Agents:
Emotional Acuity
Emotional Interpretation
Emotional Reminiscence
Emotional Learning
Emotional Articulation
Emotions will have an effect
on the following: Acuity, Viewpoints,
Reckoning, Decision Making, Action and
Expression.
4. Text Emotion Detection
Emotions play an essential facet
in the interface and communication
between populace. The barter of
emotions all the way through text
messages and forwards of private
blogs throws up the off the record kind
of writing challenge for researches.
Mining of emotions from the text
will harness for settling on the
human-computer interaction that
manages communication and many
supplementary phenomena. Emotions
are also articulated by a person’s verbal
communication, facial and wordings
primarily based emotion respectively.
Emotion detection technique at every
sentence level plays a fundamental role
to outline the emotions or to look out
the cues for spawning such emotions.
Sentences are the elemental info units
of any manuscript. For that reason, the
detection technique at the document
COVER STORY
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CSI COMMUNICATIONS | JUNE 2019
level depends on the sensations
expressed by the individual sentences
of that manuscript that in turn depends
on the emotions articulated by the
particularized words. The expeditious
augmentation of the World Wide Web
has facilitated an amplified online
communication, blog post and written
content over the websites and thus
commenced the new-fangled avenues
to identify the emotions from that
textual information. This has led to the
progress of quantified online content
affluent in user outlook, emotions,
and sentiments [4]. These needs
computational methods to successfully
investigate this online content,
familiarize, and depict constructive
conclusions and detection of emotions.
The existing schemes apportion with
the divergence identification of feelings
which tend to be positive or negative.
5. Archetyped Environment f
Simulated Agents
AI is about realistic interpretation:
reasoning in order to act constructively.
A blend of perception, reasoning, and
acting encompasses a driving force
which acts in an environment and
may include other agents. An agent
collectively with its environment is
entitled as‘world’.
Nowadays, AI is progressively more
reliant on cloud computing with its
tenacity to build up the dynamics of the
human based emotions. Diverse info
graphic database, frameworks, annals,
applications, toolkits, and datasets in
the AI and machine learning world are
into existence. The human generation
“plugs into” higher intelligence by
conceiving an unswerving neural
intersection across the internet. The
five components of AI with emotional
intelligence are as follows: big data
compilation, deep learning, self-
awareness, security and ethics and
external responsiveness (shown in Fig.
5). The human intelligence comprises
of Emotions as an indispensable
constituent and AI remains a curtailed
phenomenon sans emotional
intelligence.
6. Conclusion
Design and Development of
technology tools endowed with
emotional intelligence are the trending
research focus booming up in artificial
intelligence domain. The global outlook
is that AI imbibed into automation/
robotics is going to revolutionize the
markets and workforces. The recent
statistics suggest that Autonomous
cars will coerce over three thousand
manual drivers to seek new diverse
employment opportunities, and robotic
production lines likeTesla’s will binge
on manufacturing jobs, which are
currently at 12 million and falling.
This marks the onset of Disruptive
technology. Improvisation in AI which
is escalating to leaps and bounds will
give rise to smarter “thinking” jobs than
monotonous “doing” jobs.
References
[1] Thorndike, E.L. 1920. Intelligence and its
use. Harper’s Magazine, 140, 227-235.
[2] Kihlstrom, J., and Cantor, N. Social
Intelligence. in R.J. Sternberg (Ed.),
COVER STORY
Abilities
Goals/Preferences
Prior Knowledge
Observations
Past Experiences Actions
Agent
Environment
Fig. 4: Networking of an agent with the Setting
Deep
Learning
Safety
and
Ethics
Safe
Awareness
Big Data
Collection
External
Awareness
Artificial
Intelligence with
Emotional
Fig. 5: Artificial Intelligence with Emotional Intelligence Models
17
CSI COMMUNICATIONS | JUNE 2019
Handbook of intelligence, 2nd ed. (pp.
359-379). Cambridge, U.K.: Cambridge
University Press, 2000.
[3] Salovey, P. Mayer, J.D. 1990. Emotional
intelligence. Imagination, Cognition,
and Personality, 9, 185-211.
[4] Balakrishnan. S and K L
Shunmuganathan. Article: “A JADE
Implementation of Integrated Agent
System for E-Mail Coordination
(IASEC)”. International Journal of
Computer Applications58(5): 5-9, Nov.
2012.
[5] Balakrishnan. S and K L
Shunmuganathan, R. Sreenevasan,
“Amelioration of Artificial Intelligence
using Game Techniques for an
Imperfect Information Board Game
Geister” International Journal of
Applied Engineering Research (IJAER).
ISSN 0973-4562. Vol 9, Number 22
(2014) pp. 11849-11860.
[6] Balakrishnan. S and K L
Shunmuganathan, “An Agent-Based
Collaborative Spam Filtering Assistance
Using JADE”, International Journal of
Applied Engineering Research, ISSN
0973-4562, Volume 10, Number 21
(2015) pp 42476-42479.
[7] S. Balakrishnan, “An Overview of Agent
Based Intelligent Systems and Its
Tools”, CSI Communications magazine,
Volume No. 42, Issue No. 10, January
2019, pp. 15-17.
n
About the Authors
Dr. S. Balakrishnan (CSI Membership No. 2060000034) is a Professor at Sri Krishna College of Engineering and Technology,
Coimbatore, Tamilnadu, India. He has 17 years of experience in teaching, research and administration. He has published over
15 books, 3 Book Chapters, 11 Technical articles in CSI Communications Magazine, 1 article in Electronics for You (EFY)
magazine, 3 articles in Open Source for You Magazine and over 100 publications in highly cited Journals and Conferences.
Some of his professional awards include: 100 Inspiring Authors of India, Deloitte Innovation Award, Cash Prize ` 10,000/-,
from Deloittee for Smart India Hackathon 2018, Patent Published Award, Impactful Author of the Year 2017-18. His research
interests are Artificial Intelligence, Cloud Computing and IoT. He has delivered several guest lectures, seminars and chaired
a session for various Conferences. He is serving as a Reviewer and Editorial Board Member of many reputed Journals and
acted as Session chair and Technical Program Committee member of National conferences and International Conferences
at Vietnam, China, America and Bangkok. He has published more than 6 Patents on IoT Applications.
Dr. J. Janet is working as a Principal of Sri Krishna College of Engineering and Technology and active member of CSI. Her
specialization is Knowledge Based Systems. She has produced 13 doctorates so far and presently guiding 4 Ph.D. research
scholars. She has published over 100 papers in International refereed journals and has 176 Google Scholar citations with
h-index 8 and i10-index 7. Dr. J. Janet has executed several research projects to the tune of ` 60 Lakhs with funding from
various national agencies including DST and AICTE-TAPTEC in the areas of Artificial Intelligence and Cloud Computing. She
has mentored several research projects under UGC-MRP and DST-CSRI schemes in her previous tenure. She has conducted
several seminars, workshops and conferences with seminar grant from DST, TNSCST, NCSTC and DBT.
Dr. S. Sheeba Rani is an Associate Professor in Department of EEE at Sri Krishna College of Engineering and Technology,
Coimbatore. She has completed her Undergraduate studies in Instrumentation and Control Engineering and Post Graduate
Studies in Embedded System Technologies from College of Engineering, Anna University, Chennai and additionally Masters
in Business Administration in Human Resources Management from Madras University. She has received her Ph.D in
Engineering Education from NITTTR, Chennai. With 15 years of academic experience in premier Institutions of Tamilnadu,
she has authored more than 50 scientific publications in premier indexed journals, books and patents. Her research interests
are in the field of Outcome based education, Embedded Instrumentation and Medical Electronics. She is a professional body
member of IACSIT, IAENG and ISTE.
COVER STORY
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