JASC: Journal of Applied Science and Computations ISSN NO: 1076-5131

A SURVEY ON VARIOUS IMPLEMENTENTION TECHNIQUES

Ch.Sitha Mahalakshmi, T.Sharmila, S.Priyanka, Mr.Rajesekhar Sastry,Dr. B V Ramana Murthy and Mr.C Kishor Kumar Reddy.

Stanley College of Engineering and Technology for women-Hyderabad

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

ABSTRACT

Today is the era of intelligence in various machines. With the advances in the technology, machines have started to portray different human attributes. A is a type of application which is generated by computer. It is capable of having virtual conversation with the user in such a way that they don’t feel like they are actually talking to the computer. Chatbots will provide a simple and friendly way for customers to ask or send the information instead of searching through the websites. In this survey we describe , history of chatbot, applications of chatbots.

Keywords: Chatbots, AIML, Eliza, ALICE, Knowledge based.

1.Introduction In this quickly growing world it is necessary to have a different ideology to access the information rapidly in a simple way. Artificial intelligence is a way of making a computer thinks intelligently in the similar manner the intelligent human think. It is a science and technology based on disciplines such as , biology, psychology, linguistics, mathematics and engineering. Artificial intelligence technique is a manner which provides knowledge efficiently in such a way that  It should be graspable by the people  The errors should easily detectable and corrected

To achieve the collaboration between user and machine they have introduced a new conversational entitie called as chatbot. Chatbot is computer program capable to carry a friendly conversation the user. A chatbot is an intelligent conversation between human and machine. The term chatbot is a synonym with text conversation but growing through voice communication. The chatbot can have conversation with you through different channels like Siri, facebook, Skype, messenger, we chat, telegram, slack and many others.

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for different purpose such shopping customer service, news, games and many others. A chatbot is also known as smartbot, chatterbot, imbot, talkbot, bot, interactive agent, conversational interface artificial conversation entities. Chatbot are typically used in dialogue systems for various practical purposes.

2. History of chatbots

The history to chatbots has being developed since1950, when published his ―Computing Machinery and Intelligence‖. The paper published by Alan Turing is regarded as one of the basic foundations of Artificial Intelligence and the he proposed in this paper can be Considered as a benchmark for assess the worth of computer system intelligence .The fame of his proposed test has become a scrutiny to ‘s program Eliza which was introduced in 1966 at the MIT AI Laboratory.

Eliza pretend to have a simple, text based conversation between a user and the computer. Joseph Weizenbaum‘s main intent in creating Eliza was to manifest the superficies the interaction between user and machine .Eliza's key method of operation which recognizes the prompt words or phrases in the input, and the output of corresponding pre-programmed or pre- prepared responses that can move the conversation a head in an apparently meaningful way. Thus an apparition of understanding is initiated, even though the processing involved has been entirely peripheral. Eliza showed this illusion is easy to generate and astonishingly proven that it is ―intelligent. However, he did not contemplate the possibility of how a lot of people can easily attributed human-like feelings to the program.

Table:2.1 The history of chatbot

1950: It all began in 1950, when an English computer named Alan Turing, threw down the gauntlet by publishing an article entitled ―Computer Machinery and Intelligence.

1966: Eager computer scientist and contemporaries of Turing tried to pass his test. Eliza created in 1966 by Joseph Weizenbaum. Eliza was one of the first chatbots in the history although Eliza was able to fool some users into thinking that they were actually talking to a human; Eliza failed the Turing Test of Alan. Despite that, the principles used in Eliza laid a foundation for the structures of chatbots, such as specific phrases, keywords and preprogramming responses.

1972: introduced PARRY in 1972, a chatbot that could replicate a person with paranoid schizophrenia. In an experiment given to , only 48% were able to identify the difference between PARRY and a real person.

1995: A.L.I.C.E., a popular online bot was and a processing bot. Although A.L.I.C.E. was unable to pass the Turing Test, she did receive many other rewards for being the most advanced bot of exhibition time.

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2001: That is, until Smarter child came out. In many ways, it was the progenitor to Apple‘s Siri and Samsung‘s S Voice.

2010-2015: Over the next decade or so, bots became very popular among big tech companies, starting with in Siri introduced in 2010, Google Now in Alexa in 2015 and Cortana in 2015. These bots can able to give response to voice commands, play audio and video, and perform internet searches, and many other tasks.

Present: Amazon wants to improve Alexa by making it an intelligent socialbot that can have interaction about anything with anyone. The students at BYU have been building on the work of past computer scientists to take AI to the next level. Before, chatbots ran on keywords and specific phrases; now, they are creating chatbots that function based on neural networks and machine learning. It is incredible to see the progress made with AI, and it is exciting to think about the progress that will come in the future

The Latest in Chatbot History: From Messaging Apps to Operating Systems. Before the West would fully embrace chatbots in messaging apps, we‘d talk to ones built into our mobile devices. Siri was the first mainstream assistant built natively into a mobile OS, released in 2010. With Siri, users could interact via text or voice, performing many tasks easily via natural language. Like other mobile assistants to follow, Siri‘s feature has become a great extent into tasks that could be performed across the device‘s operating system, including apps that users can installed on their devices.

Future: Our history of chatbots takes us to the present-day and near-future. In the latest development, mobile assistants are transitioning to other devices like smart speakers. Similarly, niche bots you‘re used to talking via messaging apps which are becoming voice apps. In these upcoming technology no longer just assistants for accomplishing tasks, chatbots are serving as the primary boundary for managing a whole collection of connected devices.

And it‘s not just smart speakers in the home, either. Voice assistants and chatbots are in advance traction in wearable devices from the Siri-powered Apple Watch to the Google Assistant- powered Pixel Buds. Together with the Internet of Things, chatbots are set to modernize the way we intermingle with everything around us, perhaps ultimately cutting out the need for computers or mobile phones for the middling consumer. The future looks bright for chatbots, and we‘re eager to see where things are headed next!

3. Survey on Chatbot

It is also difficult to get all the valuable information you desire from passionate attempt at being shared. Anytime a knowledge falls somewhere in the middle, it rarely gets an over the phone or online, surveys can feel impersonal and not of particular value to the customer or responder.

3.1 Customers Prefer a Survey Chatbot for Conversational Forms

We are able to offer a better experience for the user by using a chatbot for surveys we create a virtual interviewer and make conversational surveys. Users are able to answer questions by

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using their available or favorite platforms, such as messenger, facebook. By deploying a messenger Facebook survey bot, they ensure that users never have to leave the application to provide valuable feedback.

In collaboration with YouGov a research is conducted at Michigan State University, and tested. YouGov‘s traditional online email-based pane vs. a set of consumer electronics surveys administered on Facebook Messenger. The survey showed that 76% of people participated the survey via Facebook Messenger, and those who did rated the overall survey considerably higher than the web-based one.And, according to Quick Tap Survey, 64% of customers would rather text than call a business and 40% of customers prefer self-service to interacting with a human. These survey results support the proposal that people are more inclined to give us the feedback we are looking for via a surveybot, as opposed to other survey methods.

3.2 How a Chatbot Survey Benefits Your NPS

In dealing with surveys, many companies are looking to gather information on their NPS, or Net Promoter Score. This is the industry standard in assessing your customer‘s experience and is therefore very important to pay attention to.Survey chatbots have a variety of use cases that can help with your customer‘s experience and keep your NPS score on the rise. Creating a survey with a user-friendly platform such as Messenger Facebook is one way to acquire the information you need to examine your customer experience and make it more optimistic. A great customer service experience must be established for maintaining a high NPS score. For this, chatbots for surveys are perfect. For example, you can create survey bots for customer troubleshooting and FAQs. These will offer conversational forms of online for interaction with the user and will provide customers with the immediate answers they need. They are also a great way of freeing up your customer service team for cases that are more composite and need further more attention.

4. Conversational Surveys are the Future

The users really want to feel like they can be in communication with your business in an instant. Since this isn‘t always realistic, this can be achieved by a chatbot survey. As it offer the attention customers desire, without any 24-hour manpower. By utilizing facebook messenger survey bots and as tools for getting information from customers, you can transform your entire customer experience form feedback chatbots better.

Table 1: Survey on chatbots Chatbot Year Description Creator Type

Eliza 1964 It examines the text given by user as Joseph Text input for certain keywords. Then it Weizenbaum transforms the input into a response.

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PARRY 1972 Parry was described as ―Eliza with Kenneth Text attitude‖. Psychiatrists analyzed a group Colby of real patients and computers running with PARRY.

JABBER 1982 The main aim of this chatbot is to move Text WACKY from a text based conversation to wholly voice operated system

ALICE 1995 This chatbot matches the input against Richard Text predefined set of responses. It cannot Wallace answer all the queries as it contains predefined responses

WATSON 2006 This chatbot is a question answering IBM Text system.Itwonthejeopardy! Competition in 2011.

Siri 2011 This chatbot works as an intelligent Apple Text- personal assistant. It is a part of Apple voice operating systems. It uses a natural language as an interface to answer questions of user

MITSUKU 2012 This chatbot uses AIML language to Steve worswick Text understand and respond to user It won two Loebner prizes in 2013 and 2016

ALEXA 2015 This chatbot also uses natural language Amazon Voice for voice interaction between users. It can also control several other smart devices using.

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TAY 2016 An artificial intelligent chatbot that Microsoft Text released provocative tweets caused controversy and shortly taken offline after.

4.1 Eliza

Eliza is the premature natural language processing computer program which was introduced by Joseph Weizenbaum at the MIT Artificial Intelligence laboratory from 1964 to 1966 .Which is Generated to reveal the superficiality of communication between users and machines, Eliza counterfeit conversation by using a 'pattern matching' and substitution methodology that gave the users a deception of understanding on the part of the program, but had no built in structure for contextualizing events. Instructions on how to interact were provided by 'scripts', originally written in MAD-Slip, which allowed Eliza to progress the user inputs and engage in dissertation following the rules and directions of the script. The most renowned script, DOCTOR, replicated a rogerian psychiatric that it can features the dialog between a user and the machine and used rules, dictated in the script, in such a way that it can be able to retort with non-directional questions to user inputs. As such, Eliza was one of the first chatbots and is capable of attempting the Turing test. The original program was instigated on the IBM 7094 of the Project MAC time- sharing system at MIT and was written in MAD-SLIP.

Eliza performs best when its human communicator is initially instructed to "talk" to it, by the typewriter of course, just as one would to a . This genre of conversation was chosen as one of the few examples of characterized dyadic natural language communication in which one of the contributing pair is free to undertake the pose of knowing almost nonentity of the real world. It is important to note that this hypothesis is one prepared by the speaker. Whether it is accurate or not is totally separate question. In any case, it has a decisive psychological efficacy in that it serves the speaker to maintain his sense of being perceived and unstated. The speaker further defends his impression (which even in real life may be illusory) by attributing to his conversational partner all sorts of background knowledge, insights and reasoning ability. But again, these are the speaker's contribution to the conversation. As Eliza Bot is a totally self-contained object and instances use their own internal memory it's possible to have multiple instances of the Eliza Bot object talking to each other. Eliza Bot is also a general chatbot engine that can be supplied with any rule set. The structure of ―Eliza keywords‖ follows the internal data model prescribed in the Joseph‘s article.

4.2 A.L.I.C.E

A.L.I.C.E. or Artificial Linguistic Internet Computer Entity is an esteemed artificial intelligence natural language processing chatbot. In other word, ALICE as it commonly called, as a bot program that engages in a conversation with a human by applying some heuristical pattern matching rules to the human‘s input, and in its online form it also relies on a hidden third person. It was inspired by Joseph Weizenbaum‘s classical Eliza program, and was named A.L.I.C.E because the computer that ran the first version of the software was called Alice.

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A.L.I.C.E. is also one of the strongest programs and ‗most human‘s of AI chat robot or chatbot, and has secured the Loebner Prize three times in the years 2000, 2001 and 2004. The program is unable to pass the Synthetic Game more commonly known as the ―Turing Test‖, as even the casual user will often expose its automatous aspects in short conversations. Richard Wallace ALICE‘s had developed and was rewritten in Java beginning in 1998. The program uses an XML chema called AIML which stands for Artificial Intelligence Markup Language, A.L.I.C.E is an award winning open source natural language artificial intelligence chat robot which utilizes AIML to form retorts to queries for specifying the empirical conversation rules, which was published in 2001, and controlled more developers to subsidize to the project. Alice bot, an engine or software agent of ALICE utilizes AIML. The AIML files will store the main knowledge basis of ALICE; ALICE is available to the public for free under the GNU license. 4.3. Jabberwacky

Jabberwacky is a type of chatbot created by British programmer Rollo Carpenter. It specified aim is to "pretend natural human chat in an interesting, enjoyable and humorous manner". It is an early attempt at creating an artificial intelligence through human interaction. The stated purpose of the project is to create an artificial intelligence that is capable of passing the Turing Test. It is designed to impressionist human interaction and to carry out conversations with users. It is not designed to carry out any other functions. Unlike more traditional AI programs, the learning technology is projected as a form of entertainment rather than being used for computer support systems or corporate representation.Recent developments will allow a more scripted, controlled approach to sit atop the general conversational AI, aiming to bring together the best of both approaches.The final objective is that the program move from a text based system to be wholly voice operated sound and other sensory inputs. The developer believes that it can be unified into objects around the home such as robots, intending both to be useful and entertaining or talking pets, keeping people company. 4.4.Watson

Chatbots are conversational robots that simulate conversation, and can interact with both users and services. By connecting the power of the cloud and cognitive computing, you can open up your chatbot to countless creative applications. IBM Watson Assistant is a white label cloud service that allows enterprise-level software developers to embed an artificial intelligence virtual assistant in the software they are developing and brand the assistant as their own. IBM on this cloud service gives access to this AI with full of customization. In this part, we will present the basic steps to configure a workspace and how to find all the needed data to use the API service. 4.5. Mitsuku

Mitsuku is a chatbot generated from AIML technology by Steve Worswick. It receives a four- time Loebner Prize winner in the following years 2013, 2016, 2017, 2018. Mitsuku is available as a flash game on Mouse breaker Games and on Kik Messenger under the username "Pandora bots", and was available on Skype under the same name, but was removed by its developer. 4.6. Tay

Tay was an artificial intelligence chatter bot which was formerly released by Microsoft Corporation via Twitter on March 23, 2016.It caused succeeding dispute when the bot began to post confrontational and belligerent tweets through its Twitter account, forcing Microsoft to shut

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down the service only 16 hours after its launch. According to Microsoft, this was caused by trolls who "attacked" the service as the bot made replies based on its interactions with people on Twitter. It was soon replaced with Zo. The bot was created by Microsoft's Technology and Research and Bing divisions, and named as "Tay" after the acronym "thinking about you". Tay was intended to personate the language patterns of a 19-year-old American girl, and to learn from cooperating with human users of Twitter. Microsoft has, comprehensibly, been reluctant to talk about Tay. Tay was supposed to learn from its time online, growing smarter and more aware as people on social media fed it information the idea behind Tay, which wasn‘t Microsoft‘s first chatbot, was appealing straightforward. The bot‘s front facing purpose was to be a creative distraction. But Tay had another purpose — teaching researchers more about how A.I. interacts with a massive number of people on the Internet. 4.7. Parry Kenneth Mark Colby created a natural language program called ―Parry‖ at the Stanford Artificial Intelligence Laboratory that pretends the thinking of a paranoid individual. Parry was the first to pass the Turing Test - it was in the early seventies. When human interrogators, interacting with the program via remote keyboard, were unable to distinguish the accuracy of Parry from an actual paranoid individual. Additional comments by developer Fifty years ago there was only one psychiatrist thinking about the ways in which computers could contribute to the understanding of mental illness: Kenneth Mark Colby. Thus began a project that lasted until his death in 2001. Colby practiced for the first several decades of his career, and was clinical associate at the San Francisco Institute of Psychoanalysis. But Colby became disenchanted with psychoanalysis because, in his view, it failed to satisfy the most fundamental requirement of a science. In 1967 the National Institute of Mental Health recognized his research potential when he was awarded a Career Research Scientist Award. Parry did so in the early seventies, when human interrogators, interacting with the program via remote keyboard, were unable with more than random accuracy to distinguish Parry from an actual paranoid individual. This thinking entails the consistent misinterpretation of others motives – others must be up to no good, they must have obscured motives that are dangerous, and their inquiries into certain areas must be deflected - which Parry achieved via a complex system of assumptions, attributions, and ―emotional responses‖ triggered by shifting weights assigned to verbal inputs. 4.8. Siri

Siri is a virtual assistant that is part of Apple Inc.'s IOS, watchOS, macOS, HomePod, and tvOS operating systems. To answer questions the assistant uses voice queries and a natural-language, create authorizations, and perform actions by allocating requests to a set of Internet services. The software acclimates to users' individual language usages, searches, and preferences, with continue using. Siri is a derivative from a project originally developed by the SRI International Artificial Intelligence Center. The speech acknowledgement engine was provided by Nuance Communications. Siri uses advanced machine learning technologies to process the function. Siri generally originates from American, British, and Australian voice artists recorded their corresponding voices around 2005. The voice assistant was released in the name of an app for IOS in February 2010, and it was developed by Apple two months later. Siri was then combined into iPhone 4S at its release in October 2011. Siri has since become an integral part of Apple's products, having been amended into other hardware devices over the years, including newer iPhone models, as well as iPad, iPod Touch, Mac, Air Pods, Apple TV, and Home Pod.

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Siri was originally released as a stand-alone application for the IOS operating system in February 2010, and at the time, the developers were also intending to release Siri for Android and BlackBerry devices. Siri supports a wide range of user commands, including performing phone actions, checking basic information, scheduling events and reminders, handling device settings, searching the Internet, navigating areas, finding information on entertainment, and is able to engage with IOS-integrated apps. 4.9 Alexa

Amazon Alexa, simply known as Alexa, is a type of virtual assistant developed by Amazon, first used in the Amazon Echo and the Amazon Echo Dot smart speakers developed by Amazon Lab126. It is capable of voice interaction, making to-do lists, music playback, playing audio books, setting alarms, sports, streaming podcasts, and weather forecasting, traffic and other real-time information, such as news.

Alexa has the capacity to control several smart devices by considering itself as a home automation system. Users are able to extend the Alexa capabilities by installing "skills" like additional functionality developed by third-party vendors, in other settings more commonly called apps such as weather programs, audio and other possible features. Most devices with Alexa allow users to activate the device using a wake-word other devices such as the Amazon mobile app on IOS or Android require the user to push a button to activate Alexa's listening mode. Currently, interaction and communication with Alexa are only available in English, German, French, Italian, Spanish, and Japanese. In January 2017, the first Alexa Conference took place in Nashville, Tennessee, an independent assembling of the worldwide community of Alexa developers and enthusiasts. As of September 2017, Amazon profited with more than 5,000 employees working on Alexa and correlated products.

In May 2018, Amazon announced that Alexa will be included into all of the 35,000 new Lennar Corporation homes built this year. In November 2018, Amazon opened its first Alexa-themed pop- up shop inside of Toronto's Eaton Centre, showcasing the use of home automation products with Amazon's smart speakers.

5.Applications:

Increase in the advancement of technology, chatbots have become progressively important in various domains such as educational, scientific and commercial. Chatbots can be instigated as intelligent personal assistants also called virtual subordinates on mobile devices, as artificial tutors in the educational field as they can provide social networking purview for providing personalized marketing to customers and also prompt the personalized feedback to learners. Chatbots are a big step forward in improving human computer interactions. Some of the most prominent applications of chatbots will provides personal concierge services, as financial advisors providing free legal aid personalized stylist, offering preliminary medical advice, and many more. However the extensive application of chatbots is in the field of e-commerce for systematizing customer service. Chatbots help to improve customer relations as well as significantly reduce human efforts.

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6.Conclusion We can see how the chat technologies evolved from the very simple pattern matching systems, over the statistical models of chats, towards complicated patterns in combination with ontologies and knowledge bases. It can be argued that even the newest approaches are still just a small improvement over the Eliza pattern matching idea and that the biggest improvement is the amount of scripts written for it. It is obvious that there is a trend towards semantics, which can lead to a conclusion that future chat bots will evolve from the pattern matching, towards more semantic approaches and will probably start to incorporate more and more computer reasoning systems. We agree that there is some truth in it; however it is notable that the recent developments, especially with ChatScript the chatbots are moving out of the scripted era.. Independently of Loebner competitions and other chatbot systems, IBM in 2004 started developing a question answering system, which won the show in 2011. Officially it is not a chatbot, since it is only able to answer questions, but their research presently leads into that direction as well. On top of more than 100 different text processing approaches, they used ontologies such as DBPedia, WordNet, and Yago for support to other techniques and to enable reasoning, which goes in hand with other newer chatbot approaches

References [1]”ELIZA”, Weizenbaum, Joseph (1976). Computer Power and Human Reason: From Judgment to Calculation. New York: W.H. Freeman and Company. pp. 2,3,6,182,189. ISBN 0-7167-0464-1. [2]”IEEE” 802.3 12.4.3.2.3 Jabber function [3]Jackson, Joab (February 17, 2011). "IBM Watson Vanquishes Human Jeopardy Foes". PC World. IDG News. Retrieved February 17, 2011. ELIZA”, Weizenbaum, Joseph (1976). Computer Power and Human Reason: From Judgment to Calculation. New York: W.H. Freeman and Company. pp. 2,3,6,182,189. ISBN 0-7167-0464-1. [4]“Jabberwacky” IEEE 802.3 12.4.3.2.3 Jabber function [5]Jackson, Joab (February 17, 2011). "IBM Watson Vanquishes Human Jeopardy Foes". PC World. IDG News. Retrieved February 17, 2011. [6]“Applications of chatbots” http://www.techworld.com/picture-gallery/personal-tech/9-best-usesof- chatbots-in-business-in-uk-3641500/s [7]“Turing Test” Turing, Alan (1950), "Computing Machinery and Intelligence", Mind, 59: 433– 60, doi:10.1093/mind/lix.236.433 [8]S. Ghose and J. J. Barua, "Toward the implementation of a topic specific dialogue based natural language chatbot as an undergraduate advisor," 2013 International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, 2013, pp. 1-5. Doi: 10.1109/ICIEV.2013.6572650 [9]Ly Pichponreay, Jin-Hyuk Kim, Chi-Hwan Choi, Kyung-Hee Lee and Wan-Sup Cho, "Smart answering Chatbot based on OCR and Overgenerating Transformations and Ranking," 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, 2016, pp. 1002-1005. doi: 10.1109/ICUFN.2016.7536948

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[10] N.Thomas, "An e-business chatbot using AIML and LSA," 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, 2016, pp. 27402742. doi: 10.1109/ICACCI.2016.7732476 [11] Reshmi, S. & Balakrishnan, K. Sādhanā (2016), “Implementation of an inquisitive chatbot for database supported knowledge bases”, 41: 1173. doi:10.1007/s12046-016-0544-1 [12]AIML: "AIML 2.0 draft specification released". alicebot.org. 2013-01-16

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