JASC: Journal of Applied Science and Computations ISSN NO ... · Bots can be used for productive tasks, ... Designing and building tables as ... The IBM Watson cognition services

SURVEY ON ARTIFICIAL INTELLIGENCE USING CHATBOT

G.Ratnasree1,K.Pawani2,CH.Avanthika3,Mr.Rajashekar Sastry4,DR.BV Ramana

Murthy5 and Mr. C Kishore Kumar Reddy6.

STANLEY COLLEGE OF ENGINEERING AND TECHNOLOGY FOR WOMEN

Chapelroad,Abids,Hyderabad-5000001

[email protected],[email protected],[email protected],

[email protected] gmail.com, [email protected], and [email protected].

Abstract: Artificial Intelligence Chabot is a technology that makes interaction between man and machine possible

by using natural language. In this paper, we proposed an architectural design of a Chabot that will

function at various states.A general history of a chatbot, a brief description of each chatbots is discussed.

This paper presents a survey on the techniques used to design Chabot’s and a comparison is made

between different design techniques from twenty five carefully selected papers according to the main

methods adopted.

Keywords: Artificial Intelligence, Chabot, Vpath, AgronomoBot, AIML, Pedagogical, CORDULA.

1.INTRODUCTION:

A chatbot is a computer program or an artificial intelligence which conducts a conversation via

auditory or textual methods. Such programs are often designed to convincingly simulate how a human

would behave as a conversational partner, there by passing the Turing test. Chatbots are typically used

in dialog systems for various practical purposes including customer service or information acquisition.

Some chatbots use sophisticated natural language processing systems, but many simpler ones scan for

keywords within the input, then pull a reply with the most matching keywords, or the most similar

wording pattern, from a database More specifically, a bot is an automated application used to perform

simple and repetitive tasks that would be time-consuming, mundane or impossible for a human to

perform. Bots can be used for productive tasks, but they are also frequently used for malicious purposes.

Chat bots are used in applications such as ecommerce customer service, call centers and Internet gaming.

Chat bots used for these purposes are typically limited to conversations regarding a specialized purpose

and not for the entire range of human communication. One well known example of a chat bot is ALICE.

JASC: Journal of Applied Science and Computations

Volume VI, Issue I, January/2019

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https://en.wikipedia.org/wiki/Computer_program

https://en.wikipedia.org/wiki/Artificial_intelligence

https://en.wikipedia.org/wiki/Conversation

https://en.wikipedia.org/wiki/Turing_test

https://en.wikipedia.org/wiki/Dialog_system

https://en.wikipedia.org/wiki/Natural_language_processing

https://en.wikipedia.org/wiki/Database

Fig 1:Artificial Intelligence

In 1950, Alan Turing's famous article & quot;Computing Machinery and Intelligence was

published which proposed what is now called the Turing test as a criterion of intelligence. This criterion

depends on the ability of a computer program to impersonate a human in a real-time written conversation

with a human judge, sufficiently well that the judge is unable to distinguish reliably—on the basis of the

conversational content alone—between the program and a real human. The notoriety of Turing's

proposed test stimulated great interest in Joseph Weizenbaum' program ELIZA, published in 1966,

which seemed to be able to fool users into believing that they were conversing with a real human.

However Weizenbaum himself did not claim that ELIZA was genuinely intelligent, and the Introduction

to his paper presented it more as a debunking exercise.

2.LITERATURE SURVEY:

The survey on artificial intelligence using chatbot works on various purposes and makes human life

easier in daily requirements. By Chatbot research papers we concluded that they are very useful in various

forms like communication,agriculture,medical,ecommerce,banking etc..

Fig 2:Chatbot Work.



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Chatbots never get tired. They never shut off, unless there’s some major hardware failure. They never

give in to their emotions, getting angry at customers or employees for repeatedly asking questions or

making additional queries. They are always accurate, within the boundaries of the data and information

they have access to. Consider, for a moment, the most common form of chatbot in the business world

today: the customer service tool. Customers are generally able to reach out and interact via internal

messaging channels, such as a chat window on a brand’s website. As soon as they reach out, the chatbot

responds and takes action. Customers can ask general questions, get personalized data or information

about their accounts and even engage with the chatbot like they would a normal human. It’s always on

and capable of returning useful responses, right when the customer needs.

2.1 Chatbot benefits:

The chatbot benefits at various stages as given below:

Productivity:

24/7 operations.

Higher accuracy.

Employee focus on customer services.

Cost efficiency:

30%to 60% onshore,20%offshore saving.

Ability to scale rapidly with ease.

Investment recovery 6-9 months.

Operational movements:

Ability to collect and mine vast data.

Process quality,governance,accuracy.

Meeting compliance needs.



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Fig 3:Analysis

2.2 Designing a Chatbot for Diabetic Patients:

In this paper, we proposed an architectural design of a chatbot that will function as virtual diabetes

physician/doctor. This chatbot will allow diabetic patients to have a diabetes control/management advice

without the need to go to the hospital. A general history of a chatbot, a brief description of each chatbots

is discussed. We proposed the design of a new technique that will be implemented in this chatbot as the

key component to function as diabetes physician. Using this design, chatbot will remember the

conversation path through parameter called Vpath. Vpath will allow chatbot to gives a response that is

mostly suitable for the whole conversation as it specifically designed to be a virtual diabetes physician.

This chatbot design is yet to be implemented but first, the questions and answers for virtual diabetes

control diagnosis session must be designed with the actual diabetes physician. In this chatbot design, we

proposed the used of Vpath, a way for chatbot to remember the conversation path. We also design the

conversation to be controlled by chatbot rather than by user (likes any other chatbot program) by making

the user remain to the conversation topic and not to enter any irrelevant input, and if they do, chatbot will

response that the input was not understandable and keep repeating the previous question (with a good

manner) until the keywords is detected. The suggestion also will be provided as guidance for patient in

order to correctly answers the questions. Rather than just one response for one input, this design will

allow chatbot to response to the whole conversation as it specifically designed to be a virtual diabetes

physician for early symptoms diagnosis on diabetes control activities.



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Fig 4:Diabetes Flow

2.3 Chatbot Using A Knowledge in Database:

The knowledge of chatbot are stored in the database. The chatbot consists of core and interface that is

accessing that core in relational database management systems (RDBMS). The database has been

employed as knowledge storage and interpreter has been employed as stored programs of function and

procedure sets for pattern-matching requirement. The interface is standalone which has been built using

programming language of Pascal and Java.

The analysis of this research the result‘s In making a table of database for chatbot, it had implemented a

forward-engineering technique. This technique is generating Entity Relationship (ER) into DDL scripts

those could be executed as table generating. All designs of tables and stored programs had been

implemented. The development of chatbot application in various programming language had been done

with making a user interface to send input and receive response. Designing and building tables as

representation of knowledge in the database had been started from entity-relationship diagram resulting

11 entities and its cardinalities. Making use of structured query language (SQL) for pattern matching had

been done within stored program. The stored program consists of 4 stored procedures and 21 stored

functions employed as pattern matching and supporting processes. Bigram method can be used not only

for Indonesian language words, but also other languages with some boundaries.

2.4 Proposal of Chat Based Automated System for Online Shopping:

The idea about this application is that it will help the user to interact with the Ecommerce engine

through an Intelligent Assistant. The application offers the exhilarating experience of placing orders on

the Ecommerce site according to his/her needs and viewing the previously placed orders anytime the user

wants. CartBot will turn into a customized personal assistant that knows your online likes and preferences



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and serves as a magical tool to deliver the products on time and in the most convenient manner. An

account has to be linked to the device, thus allowing you to back up your history in case of device

malfunction or any other unfortunate activity. This paper is based on the research work that has been done

for the project.

The Cartbot will use artificial intelligence and hence will learn the responses of the users resulting in

increasing efficiency. Cartbot will have the ability to respond like human being hence it will ease the

efforts that are required to be done by human. Thus, in this paper, we have planned to implement an

Ecommerce engine based Cartbot which will attempt to improve the interaction of the user with E-

Commerce engine. Cartbot will store a set of responses, but also will take dynamic user input into account

and thus tend to provide relevant responses and product suggestions.

2.5 AgronomoBot-a smart answering Chatbot applied to agricultural sensor networks:

For agricultural purposes, it is important that the data about field conditions, such as air and soil

temperature, air relative humidity, soil moisture, rainfall, wind speed and other relevant variables, be

rapid and easily available for use by farm management systems, by specialists, or the farmer itself in

decision-making processes. AgronomoBot was developed focused on the search and display of data

acquired from a Wireless Sensor Network deployed on a vineyard. It is based on Telegram Bot API and is

able to access information collected by echo field sensors, bringing it back to a user through interaction

over the Telegram application. The IBM Watson cognition services platform was also used for improving

the user experience by enabling the use of natural language during the conversation experience, providing

intention detection. Further developments are planned for AgronomoBot, such as the expansion to other

messaging platforms, the implementation of speech communication capacity, image classification and

continuous data analysis. It is hoped that with analytical capacity over the mass of available data, it

becomes possible to work towards the prevention of harmful situations to agricultural productions, early

detection of diseases in crops, energy and water waste reduction, and advanced management capabilities

for the farmer.

It was possible to achieve the objectives, presenting a satisfactory solution for the search and display

of data on a WSN applied to wine production, based on the use of natural language that combines the

functionalities of the electronic message service Telegram and the power of the cognitive services

platform Watson from IBM.

2.6 Emotion Detection in Dialog Systems: Applications, Strategies and Challenges:

Emotion plays an important role in human communication and therefore also human machine dialog

systems can benefit from affective processing. We present in this paper an overview of our work from the

past few years and discuss general considerations, potential applications and experiments that we did with

the emotional classification of human machine dialogs. Anger in voice portals as well as problematic

dialog situations can be detected to some degree, but the noise in real life data and the issue of

unambiguous emotion definition are still challenging. Also, a dialog system reacting emotionally might

raise expectations with respect to its intellectual abilities that it can not fulfill.

Asking for the purpose of uttering negative emotions in every day communication, three main

functions can be identified: Uttering negative emotions may serve to 1. inform your communication



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partner about your own emotional status in order to give him a complete comprehension of the

information you want to express(your emotional appraisal of the information given). 2. inform your

communication partner about the perceived, respectively the desired quality of relation between the

communication partner and yourself (e.g.denial or distrust) 3. induce a certain action or behavior of your

.

Fig 5:Emotion sensing

It works with respect to the analysis of emotion related states in spoken dialog contexts. Several

applications are envisaged and automatic classification/prediction based on dialog and acoustic features is

possible, although complicated by real world constraints like highly noisy data. We found that the

comparison of different classifier approaches is worthwhile and a systematic analysis of the reasons for

performance differences between classifiers will be done.

2.7 Pharmabot: A Pediatric Generic Medicine Consultant Chatbot:

It introduces a Pharmabot: A Pediatric Generic Medicine Consultant Chatbot. It is a conversational

chatbot that is designed to prescribe, suggest and give information on generic medicines for children. The

study introduces a computer application that act as a medicine consultant for the patients or parents who

are confused with the generic medicines. The researchers use Left and Right Parsing Algorithm in their

study to come up with the desired result.

Based from the acquired results of the study entitled “Pharmabot: A Pediatric Generic Medicine

Consultant Chatbot”, the researchers have come-up with the following conclusions: The acceptability of

Pharmabot: A Pediatric Generic Medicine Consultant Chatbot based on the assessment of 4th year

students of the College of Pharmacy of Our Lady of Fatima University in terms of its user-friendliness

and consistency of response are both “STRONGLY AGREE”. While the appropriateness of answer and

speed of response are both “AGREE”. The acceptability of Pharmabot: A Pediatric Generic Medicine

Consultant Chatbot based on the assessment of the Experts from St. Vincent Hospital in terms of its user-



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friendliness, appropriateness of answer, speed of response and consistency of response are all “AGREE”

According to the data gathered, analyzed and computed, the researchers showed that there is no

significant difference between the assessment of the student and experts on Pharmabot: A Pediatric

Generic Medicine Consultant Chatbot thus accepting the null hypothesis. Both respondents had different

opinion and perception concerning the different variables tested.

2.8 AIML Based Voice Enabled Artificial Intelligent Chatterbot:

It shows the implementation of an artificial intelligent chatterbot with whom human can interact by

speaking to it and receive a response by chatterbot using its speech synthesizer. Objective of this paper is

to show application of chatterbot that can be used in various fields like education, healthcare, and route

assistance. It is statistical model and chatterbot is based on AIML (Artificial Intelligent Markup

Language) structure for training the model and uses Microsoft voice synthesizer for providing speech

recognition system and natural language processing.

Fig 6:AIML

The chatter bot performed up to the marked as in case of text provided, results produce were 100

percent accurate and more reliable. But in case of voice enabled input due difference in accent of user and

Microsoft speech synthesizer model whose accent is based on UK or USA users. Thus result obtain were

close to the appropriate answer. Thus from survey it can be consider that speech synthesizer need to be

model for all kinds of accent such that it process the voice input more correctly ,therefore chatter bot

model can produce more accurate results.



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2.9 Ethical Issues in Advanced Artificial Intelligence:

The ethical issues related to the possible future creation of machines with general intellectual

capabilities far outstripping those of humans are quite distinct from any ethical problems arising in

current automation and information systems. Such super intelligence would not be just another

technological development; it would be the most important invention ever made, and would lead to

explosive progress in all scientific and technological fields, as the super intelligence would conduct

research with superhuman efficiency. Since the super intelligence may become unstoppably powerful

because of its intellectual superiority and the technologies it could develop, it is crucial that it be provided

with human-friendly motivations. This paper surveys some of the unique ethical issues in creating super

intelligence, and discusses what motivations we ought to give a super intelligence, and introduces some

cost-benefit considerations relating to whether the development of super intelligent machines ought to be

accelerated or retarded.

Fig 7:Ethical issues.

A super intelligence is any intellect that is vastly outperforms the best human brains in practically every

field, including scientific creativity, general wisdom, and social skills.1 This definition leaves open how

the super intelligence is implemented it could be in a digital computer, an ensemble of networked

computers, cultured cortical tissue, or something else. On this definition, Deep Blue is not a super

intelligence, since it is only smart within one narrow domain (chess), and even there it is not vastly

superior to the best humans. Entities such as corporations or the scientific community are not super



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intelligences either. Although they can perform a number of intellectual feats of which no individual

human is capable, they are not sufficiently integrated to count as “intellects”, and there are many fields in

which they perform much worse than single humans. For example, you cannot have a real-time

conversation with “the scientific community”. While the possibility of domain specific “super

intelligences” is also worth.

2.10 The Persona Effect: Affective Impact of Animated Pedagogical Agents:

Animated pedagogical agents that inhabit interactive learning environments can exhibit strikingly

lifelike behaviors. In addition to providing problem-solving advice in response to students’ activities in

the learning environment, these agents may also be able to play a powerful motivational role. To design

the most effective agent-based learning environment software, it is essential to understand how students

perceive an animated pedagogical agent with regard to affective dimensions such as encouragement,

utility, credibility, and clarity. This paper describes a study of the affective impact of animated

pedagogical agents on students’ learning experiences. One hundred middle school students interacted

with animated pedagogical agents to assess their perception of agents’ affective characteristics. The study

revealed the persona effect, which is that the presence of a lifelike character in an interactive learning

environment even one that is not expressive can have a strong positive effect on student’s perception of

their learning experience. The study also demonstrates the interesting effect of multiple types of

explanatory behaviors on both affective perception and learning performance.

Fig 8:Pedagogical.

deploying animated pedagogical agents on a broad scale is quickly becoming a reality. Because these

agents can provide students with customized advice in response to their problem-solving activities, their

potential to increase learning effectiveness is significant. In addition, however, these agents can also play

a critical motivational role as they interact with students. As a result, students may choose to use

interactive learning environments frequently and for longer periods of time.



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2.11 CORDULA: Software Requirements Extraction Utilizing Chatbot as Communication

Interface:

Natural language requirement descriptions are often unstructured, contradictory and incomplete and

are therefore challenging for automatic processing. Although many of these deficits can be compensated

by means of natural language processing, there still remain cases where interaction with end users is

necessary for clarification. In this vision paper, we present CORDULA, a system using chatbot

technology to establish end-user communication in order to support the requirement elicitation and partial

compensation of deficits in user requirements.

The revised version of CORDULA aims to overcome the named weaknesses of the existing system.

This already begins with the design of the web interface, which has to be changed in that a dialog between

end-users and the system is in the centre of attention and also affects fundamental system components

such as the KB or the internal communication between the system components. The idea of using a

chatbot has a strong effect on the underlying system architecture of the current version of CORDULA.

Until now, the entire processing pipeline was concentrated on a static input text. Now, new information

can be added which can also affect already processed requirements. For this reason, a requirements

manager should be installed to monitor the effects of the user dialog as well as the changes in the software

requirements made via the GUI.

3.CONCLUSION:

We concluded that the artificial intelligence using chatbots are very useful it’s easier to understand and

to develop it many ways to use it efficiently. In conclusion, the biggest advantages of chatbots include

being able to reach a broad audience on messenger apps, as well as the ability to automate personalized

messages. Artificial Intelligence is perhaps the most interesting as well as challenging field of research

today. It has already proven itself for solving some major problems for mankind. In near future, AI will

present itself on a larger canvas and will become integrated in our day-to-day lifestyle. But there is need

to continuously look for new ideas for development and to make progress in already devised research. We

will then look forward to solving such problems via intelligent systems, where human intelligence can err.

The paper offered an alternate foundation theory of intelligence in machines. The basic work can be

carried forward to develop advanced AI theories and systems. Moreover, there is utter requirement for

new theories to emerge and develop, especially in a challenging field like Artificial Intelligence.

The strength of this project was the opportunity to be among the first analysist’s of the Chat Bot concept.

However, Chat Bots being new proved to provide certain obstacles especially in terms of analytics and

user engagement, which has to be thought of as the limitation of this project.

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JASC: Journal of Applied Science and Computations ISSN NO ... · Bots can be used for productive tasks, ... Designing and building tables as ... The IBM Watson cognition services