Chatbot: Efficient and Utility-based Platform

Computing Conference 2018 10-12 July 2018 | London, UK 

Chatbot: Efficient and Utility-based Platform Sonali Chandel1, Yuan Yuying2, Gu Yujie3, Abdul Razaque4, Geng Yang5 1,2,3,4

School of Engineering & Computing Sciences, New York Institute of Technology, Nanjing, China 5 Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing, China 1 [email protected], [email protected], [email protected], [email protected], [email protected] Abstract—This paper aims to analyze the technology of chatbots and investigate its development, which is becoming a popular trend now. A chatbot can simulate a human being to interact with the people in real-time, using the natural language and sends its response from a knowledge base and a set of business rules. Firstly, By using a few examples of the famous chatbots, we have shown that the artificial intelligence based chatbots are the latest trend. The salient features of the chatbot techniques have been discussed, in short, using examples of 5 chatbot-based utilities. Then we have analyzed the significance of a chatbot. Also, we have presented people’s view of chatbots through a short survey to find if the popularity of this utility is rising or declining. The way they work and their advantages and disadvantages have also been analyzed respectively through the arrangement and analysis of information, as well as statistics and conclusions. Further, we have introduced the design principles of a chatbot. We have used the examples of some popular utilities to explain them specifically. The empirical result to create a prototype for the proposed test is shown in the form of questionnaire and recommendations. We have tried to find out a relationship between chatbot and utility. We have also presented the study of their time complexity, according to the algorithm of a chatbot. In the future, human beings are more likely to use human-computer interaction by interacting with chatbots rather than using network connections or utilities. With this research, we hope that we can provide a better understanding and some clear information for people to know better about the relationship between chatbot and utility. Keywords—Chatbot; human-computer interaction; artificial intelligence; time complexity

I.

INTRODUCTION

A chatbot (short form for “chat robot”) is a computer program that communicates with a human being through text or voice messaging in real-time, in a way that is very personalized. Many times they are also called as “bots.” Chatbots are developed using artificial intelligence and natural language processing technology. They are mainly designed to simulate a conversation environment between a program and a human being in a way that is very similar to an actual conversation between two human beings. At present, the most common areas where the presence of chatbots can be profoundly seen are, customer service centers, e-commerce, healthcare, and messaging apps. With the aid of these services, users only need to send a short message as input to get the required answers, which they get by visiting a related website or by making a phone call. Chatbots are gaining popularity very rapidly because they are much faster and cheaper to

implement than a human being who is responsible for doing the same job. Artificial intelligence (AI) is the branch of science called cognitive science. It can accomplish the specific instructions by simulating scenarios, human consciousness and thought patterns. In cognitive science, artificial intelligence is defined as: “a codification of knowledge that will finally explain intelligence” [1]. While services based on artificial intelligence are gradually improving, it still needs to develop the ways that can make an artificial conversation appear more real and more connected on an emotional level. A chatbot uses AI algorithms to process natural language and use the analysis that results from the processing to finally produce an intelligent response, based on the human input. Researchers are trying to make the human-computer interaction (HCI) more and more natural [2]. Although HCI is designed to provide a platform for users and computers to interact with each other, the final target is to use expert systems and deep learning more accurately to create a chatbot that can entirely copy the way a human would respond to a specific question or a situation. This makes it very difficult for a human being to realize that they are talking to a robot and not a real person. The importance of this paper is to give an analysis of the applied fields and development of chatbots so that people can be better prepared to welcome the arrival of chatbot era. The rest of this paper is structured as follows. Section II outlines the salient features of various chatbots. It also talks about the related techniques by using the examples of five chatbot based utilities. Section III introduces the significance of chatbots by analyzing the current situation. Section IV introduces the design principles of our proposed chatbot and uses the examples of some favorite utilities to explain them accurately. It also presents a relationship between a chatbot and utility. Section V shows the empirical result to design a prototype for a proposed test in the form of a questionnaire and recommendations. Section VI and VII gives the algorithm of chatbot and the corresponding time complexity in the best case and worst case scenario. Section VIII provides the conclusion and future of chatbots. II.

SALIENT FEATURES OF CHATBOTS

In this section, we introduce the salient features of existing chatbots.

Supported in part by the National Natural Science Foundation of China

80 | P a g e

Computing Conference 2018 10-12 July 2018 | London, UK  A. Siri: Siri is one of the most popular applications in Apple phones. It works as an intelligent chatbot which is more like your assistant. You can activate Siri by saying, “Hi Siri” and ask her questions or direct her to do something. She can give you the corresponding response regarding information or a recommendation. She can also help its user to get some simple tasks done such as making a call, sending messages, ordering a meal, or booking a flight, etc. But sometimes, Siri cannot give the accurate answers to your questions. She also has a problem in understanding various accents from different parts of the world. After the combined work with many local online services, Apple has made her a little more diversified and local in its newer versions. But it still cannot meet its user’s needs completely. B. Simsimi: Simsimi (pronounced as Shim-shimi) is a very popular South Korean chatbot. It is aimed to give interesting answers to its users during the chatting process to help them to release their stress. The working principle of AI Chatbot Simsimi can be roughly divided into two parts: Teaching + Matching. “Teaching” of Simsimi is the process of training. During this process, if Simsimi tells you that it cannot understand your question or it cannot find the answer, then there exists a way which the user can use to teach Simsimi how to do it. The purpose is to build or make a rich thesaurus. And “Matching” process consists of comparing the word given by the user and searching it in its database to find the suitable answer. The most significant difference between Simsimi and other chatbots is that it will add the users’ answers into its corpus. As a result, it is entirely possible that the answers you receive as a response to a question from Simsimi could be the answers from other users. It is one of the simplest chatbots in the world that can only provide chatting function. Also, if a user wants to teach Simsimi some slangs or explicit words that are considered offensive in a normal conversation, then it will learn it as well because of its lack of an independent mechanism to distinguish between right and wrong words. As a result, it has not been accepted by the industry as widely as other chatbots in recent years. It is mainly famous among youngsters who love its colorful presentation and the feature that allows them to stay anonymous. C. Cortana: Cortana is a Microsoft created personal assistant for Windows. Cortana works in association with Bing search engine to respond to user’s queries or requests and can be activated by saying, “Hey Cortana!”. Cortana sometimes will provide some useful information even before the user asks for it, which it obtains from its user’s emails. Such as the time of the next flight, and based on that; it can remind users to catch the plane on time [3]. This feature efficiently avoids the probability of forgetfulness. However, Cortana also has problems in understanding the localized English accent. D. Google Now: Google Now is a Google product that works on Android and iOS compatible, diverse types of devices

(although the performance of different smartphones and operating systems is slightly different). It works in association with Google search engine and can be activated by saying “Okay Google.” It has a lot of useful features such as support for voice input, SMS, and mail, providing navigation, reading the agenda, booking restaurants and searching information, etc. Understanding various local accents in English is also a problem with this chatbot. E. Facebook Messenger:  In 2016, Facebook released its chatbot through the new version of Facebook messenger which got separated from Facebook App and as a result became very popular among the users worldwide. To use this bot, the user just needs to send a text to the designated number and get various things such as booking a restaurant, ordering a meal, shopping online, etc. done. It can also give suggestions to its users like planning an event, dating reminders, sending and receiving red envelopes, location sharing, and even facial expressions according to the various scenarios it experiences on a daily basis. The more it is used, the more intelligent and the more accurate it gets. A unique feature that this bot has is the ability to allow other chatbot developers to use its language processing technology to develop their bots for free. III.

SIGNIFICANCE OF A CHATBOT

Smartphone apps are in a state of change, which gives a chance for chatbots to take over as a new developing service. Even though the download of applications is increasing, the economic market of apps has been showing a saturation phenomenon. The consulting company indicates that “the independent developer’s dream, which relies on the app store to create a business, has burst already” [4]. The cost of developing, maintaining and promoting an app is getting higher with time. Meanwhile, according to the results of our questionnaire, users’ enthusiasm is also falling because it is getting difficult to download or update some apps for free and switch between different apps. About a quarter of apps are used only once before they are deleted. A lot of users do not like the idea of installing, updating and learning new apps or they rarely use it after installing. Most of them rarely use more than 5 out of 2030 apps (on an average) on a regular basis, and the most popular ones are still messaging apps and the ones for ecommerce. As a result, it is turning out to be a costly loss for companies who spend time, money, and energy to develop these apps. Vision Mobile analyst, Michael Vakulenko declares that there’s a great need for chatbots. It is hosted by the server instead of installing on the user’s device, just like a website’s page, which reduces the cost and difficulty of development, maintenance, and update [5]. That is why chatbot appeals to enterprise users much more than the ordinary users. For them, the performance of chatbot is more welcoming. It just takes a few seconds to install chatbot, and most of them work through already installed messaging apps on users’ side, which is

81 | P a g e

Computing Conference 2018 10-12 July 2018 | London, UK  already quite high in numbers with all the popular messaging apps put together. The user does not need to click on the extra icons to switch among different chatbots. Also, talking to a chatbot is less complicated and much faster than talking to a customer service representative of a company regarding getting a response to a question or a specific recommendation based on a particular need. The main reason behind this delay is because, in spite of an organization spending millions to keep their system updated when it comes to technology, the customer service representative will still have a limitation, regarding the speed with which they can work as a human. However, it takes time to make users completely trust services like a chatbot. Recently, Microsoft designed a model to imitate the millennial generation robot, Tay [6], but soon, it learned dirty words from some Twitter users and other chatting servers. As a result, Tay ended up being rebuilt. Chatbots always provide answers by working together with various search engines (based on their brand) which sometimes does not appeal to the users because it makes them feel like chatbots are short of intellectual functions. The lack of advisory services on an emotional level is also one of the weaknesses that chatbots need to improve in the future. A. Current Situation In the utility, what the user can only gain is the solid service content. However, with the chatbot, the user will regain service option by automatic message response. Users can ask frequent questions from chatbot and get the corresponding response or recommendations in real-time. Also, the chatbot can automatically add the relevant contents and data model according to user's questions, which allows companies to understand what data content their customers need and analyze the services and products based on their need. That is the function the utility does not have. Until Apple and Google started to develop its personalized app store, the utility economy was indeed at a rapid development and growth phase. The economics of chatbot also needs the leadership of giant companies. In fact, Microsoft and Facebook are hoping to play that role in the present times. Apple or Google control most of the operating systems of smartphones at present. The robot, by contrast, the market has yet to happen. Facebook is expected to open the chatting platform for all kinds of robots and launch an online store dedicated to showing these services. Currently, the start-up companies associated with chatbots are growing quite rapidly. Thanks to all these tech giants like Microsoft, Google, and Facebook who are trying their best to make chatbots accessible by providing free advanced development tools and framework to the developers. The effort from these companies has resulted in making the job of developers very easy in creating a better chatbot as they can focus more on making a chatbot more accurate and more real for users by using advanced AI, deep learning, machine learning, speech recognition and other natural language processing techniques.

Chatfuel, for example, is considered to be one of the most user-friendly, free bot platform for creating an AI-based chatbot on Facebook. It claims to let a developer or anyone for that matter create a chatbot in just 7 minutes without using any coding. Facebook even allows any company to integrate their bots with its messenger app thus making the app very efficient and versatile regarding searching and looking for specific information and getting recommendations for some specialized services. Pana and SnapTravel are few of the examples of such collaborations between Facebook and a lot of hotels and travel websites that makes a user’s life much more convenient by getting what they want so comfortably. B. Future In spite of all these tech giants trying their best to make chatbots more user-friendly and famous, no one can still guarantee that the chatbots will be as successful as smartphone apps are. According to the estimation of Progressive Policy Institute, the latter has created 3.3 million jobs only in the United States and Europe. For developers, the appeal to chatbot economy is not so obvious yet as compared to utilities. If a chatbot is too easy to develop, then it also means that it will be more competitive. Furthermore, users may still be surrounded by a large number of services and user-friendly interactive ways, and thus feel chatbots to be more of an emotionless interference than anything else. Besides, designing a more realistic text or voice interface may not be very easy. After Slack launched the first edition of its bot service, Matty Mariansky, who is the co-founder of MeeKan (AI scheduling bot), was shocked using user-diversified communication. He even hired a scriptwriter who came up with a total of 2000 multiple sentences to deal with a meeting request. The increasing popularity of chatbots shows that people are willing to work with robots. According to a recent Gartner report, by 2020, an average person will have more conversations with AI-enabled bots than they will have with their spouse [7]. But its success largely depends on the “killer chatbot,” i.e., the service that is the most suitable for the existence of chatbots and is more popular. Toby Coppel, who owns a venture capital firm, believes that health care is an upand-coming market for chatbots. Chatbots can handle patients with routine diseases very well while doctors can take care of more serious, incurable diseases. Chatting application Kik launched a “robot shop” on April 5. The company founder, Ted Livingston expects that “instant interaction” will be the key to its success. He believes that the future of enterprises not only needs a phone number and web pages as their business ids, but it would also require a mention of having their chatbots. Restaurants through instant messaging platforms and bots can receive an online order for home delivery. In fact, many restaurants in China has already started to provide such services, and they are running very successfully. The chatbots also need to go through a lot of smart explorations to find their positioning as they depend on the ability of the providers managing their platform. Telegram allows developers to be engaged in almost all the development work (but they have closed their chatting channels in most of the Islamic countries). Microsoft also promises to remain open as much as possible. Developers and investors have also got

82 | P a g e

Computing Conference 2018 10-12 July 2018 | London, UK  some scruples about Facebook’s initiative in this domain because the company has previously had a changeable history, leading the companies which developed apps for their websites, into trouble. There are some companies which are hoping to become a foundation for the survival of other services. Assist wishes to play the role of “Google” for chatbot users by helping them find available chatbots as per their needs. Another company named Operator wants to become “Amazon” in the field of chatbot business. For example, when a user is looking for some sneakers, the system will contact the nearest salesman or let their own “experts” deal with the order. Operator’s boss Robin Chen (Robin Chan) hopes to create a virtuous cycle with more buyers to attract more merchants, with more businesses in turn to attract more buyers. Devices such as Google Home and Amazon’s Echo are increasingly being used to interact with machines without using a screen. Gartner also predicts that by 2020, 30% of browsing sessions will be done via a screen-less interface. Advances in Natural Language Processing (NLP) and Natural Language Generation (NLG) mean that AIs such as IBM’s Watson can interact and respond in ways that are increasingly indistinguishable from person-to-person communication [8]. IV.

recognition, dialogue strategy of learning, dialogue state recognition, and dialogue awards.

Fig. 1. Design principles of chatbot.

Language Generation: Natural Language Generation is usually based on the non-verbal information produced by the part of conversation management. It automatically generates user-oriented feedback of natural language. In recent years, the dialogue generation of the conversation in the chatbot system mainly relates to the process of retrieving and producing two types of technologies.

D. Natural

DESIGN PRINCIPLE OF THE PROPOSED CHATBOT

Based on all that is currently going on in the field of chatbot development, we are proposing a chatbot prototype through this paper. The system framework of our proposed chatbot system is shown in the diagram below (Fig. 1), which is made up of five functional modules. Automatic Speech Recognition is responsible for converting the voice signal from users into the form of text. Natural Language Understanding module processes the message after receiving the text. After understanding the semantics of users’ input, Language Understanding module transfers the specific semantic expression to Dialogue Management module. Dialogue Management module is responsible for the coordination of calls between various modules and maintains the current conversational state. It chooses a particular way of replying and gives it to the Natural Language Generation module for processing. A. Automatic Speech Recognition (ASR): It is a technology that allows a human being to interact with a computer interface through their voice in such a way that it seems very close to the actual human conversation in spite of having various accents in their speech. B. Natural Language Understanding: Natural Language Understanding is related to machine reading comprehension. The process of taking parts of sentences and analyzing the meaning is complicated because the machine needs to determine the correct syntactic structure and semantics of the language used. C. Dialogue Management: Dialogue Management function is mainly responsible for coordinating the various components of a chatbot and maintaining the dialogue structure and its state. The key technologies involved in this feature among many are dialogue behavior

V.

EMPIRICAL RESULTS TO DESIGN A PROTOTYPE FOR THE PROPOSED TEST

A. A Subsection Sample We prepared a questionnaire to analyze the current situation of chatbots and applications. The result of the survey is shown in Table I. TABLE I.

QUESTIONNAIRE ABOUT CHATBOT FEATURES

Questions

Response Online shopping

What function of a chatbot have you used?

Make restaurant reservation Order tickets Lively, interesting and lovely

What`s your first impression of a chatbot?

Intelligent and high-tech Well, maybe still need to improve

What do you think about the dialogue functions? What aspects of chatbot need to improve? Please suggest. What is the advantage of using a chatbot?

What is the disadvantage of using a chatbot?

Lively and interesting The chatbot can respond to our questions accurately The lack of intellectual functions The lack of advisory services Human-computer interaction Transform something into an integrated service platform We need to download a variety of apps Some apps are not practical.

83 | P a g e

Computing Conference 2018 10-12 July 2018 | London, UK  After the survey mentioned above completed, we organized a face-to-face meeting, in which all the participants came and shared their experiences and gave their impression regarding the test of our chatbot. We also got suggestions regarding the modification and improvement of our chatbot. Based on the participants’ feedback, we compiled the results and arranged it in the form of a table for better understanding (shown in Table II). B. Recommendation Chatbots should have the ability to analyze the data sets and create links between these data. They should be capable of automatically generating more accurate and convincing answers for the users, building a better understanding of the response to the questions that are being asked, to be able to project a better understanding of the context of the conversation. Chatbots should continuously update their dataset to create more elaborate answers and knowledge of the parameters used in the conversation as input. It should be able to search for further analytical service source information and establish the most reliable connection for the HCI between chatbots and humans. Chatbots, which perform the function of customer service, have a substantial role to play in creating an impact on its overall benefits. If they can be widely used in the future successfully, then they would be able to control almost the entire service industry’s domain. People in the future will be able to interact with machines by using Automatic Speech Recognition without using a screen. TABLE II.

Should be supposed to use?

18

02

00

00

00

Should be a user-friendly interface?

19

00

01

00

00

Neutral

Negative

Strong Negative

Fig. 2. The flow chart of the proposed chatbot.

We also generated an algorithm depicting the working logic of the proposed chatbot which is as follows: Algorithm 1：Word-segmentation Process 1.Initialization {dic ＝ Dictionary, Dt = Decompose the sentence, St = Segmentation text, Se = Sentences, β = dealing sentences, δ

= available}

2. Input {Se: Sentence} 16

02

01

01

00

3. Output {St} 4. Set dic // loading dictionary 5. For St = 1 to n 6. Do St // obtaining segmentation text

To put functions of apps together

17

To be more practical

18

To help users improve working efficiency

The answers we get from a chatbot comes from its database which uses the concept of AI and language processing techniques to establish a more personalized response. So, we need to find out how to find the answer corresponding to a particular question. After doing our analysis, we found that the segmentation of sentences is critical. We can use the space to recognize the difference between words. The flowchart of the proposed chatbot is depicted in Fig. 2 that shows the process to get the key phrases.

 

Positive

To reduce the difficulty of developing and updating app

PROPOSED CHATBOT

PRE-USABILITY TESTING OF PROCESS

Strong Positive

To give more convenient daily services for people

VI.

02

01

00

00

7. End for 8. Process Dt // decompose the sentence

02

00

00

00

9. If Se = β then 10. Display St 11. Else if do maximum process

15

03

02

00

00

12. Check ambiguity 13. If ambiguity = δ, then // Is ambiguity available 14. Resolve ambiguity, otherwise 15. go to step 7

16

03

01

00

00

16. end if 17. end else if 18. end if

84 | P a g e

Computing Conference 2018 10-12 July 2018 | London, UK  TABLE III.

The complete working process of the algorithm can be explained as follows: Steps 1-3 show initialization of input and output respectively. In Step 4, the loading process of the dictionary begins. Steps 5-7 show the sentence checking and segmentation process. In Step 8, the decomposition to split the long sentences into small sentences takes place. Steps 9-10 deal with the sentences and their display. Step 11 uses the maximum matching technique to find the longer phrases in the split parts of sentences, which is used as the keyword to search the corresponding answer in the database. Steps 12-14, determine the ambiguity of the phrases. If ambiguity is found, then it is addressed. Step 15 checks whether any ambiguity exists in any sentences. If not, then the process of dealing with the sentences is initialized in Step 15. VII.

Time complexity of proposed approaches

The results show that our proposed chatbot has the time complexity of O (log n) and it took 0.08 seconds to complete the analysis of 80 kilobytes of the input file. The results confirm that our proposed chatbot provides better improvement compared to the other known, existing chatbots. Our proposed chatbot has lowest time complexity because of the use of the word segmentation that helped to reduce the time complexity. The time complexity analysis of our proposed chatbot and other known chatbots are given in Table III.

Time complexity T (n) = T (n − 1) + T (0) + O(n)

Facebook Messenger

= T (n − 1) + O(n) = O(n2) 𝑛 𝑂 𝑛 𝑏 Problem consists of finite set of inputs, but 𝑇 𝑛

MATHEMATICAL ANALYSIS AND COMPLEXITY

The time complexity can calculate the performance of any chatbot. The time complexity refers to the total amount of execution time reserved to run as a task signifying the input. Also, the time complexity is measured by calculating the number of primary operations accomplished by the algorithm, and a central procedure takes a constant amount of time to execute. The lesser is the time, the higher is the efficiency. In Fig. 3 below, we show the trend of the time complexity of our proposed chatbot and comparison of its time complexity with other famous chatbots, namely, Simsimi, Siri, Cortana, Google Now and Facebook Messenger.

TIME COMPLEXITY OF OUR PROPOSED AND KNOWN CHATBOTS

Proposed Chatbot

𝑎𝑡

computation complexity remains constant ‘n’ 𝑛 𝑂 𝑛 𝑇 𝑛 𝑡 2 𝑛 𝑇 𝑛 𝑡 𝑛 2 𝑛 𝑛 𝑛 𝑡 𝑛 𝑇 𝑛 𝑡 1 𝑛 𝑇 𝑛

𝑡

𝑛

Where ignore 𝑡; therefore, we get 𝑇 𝑛

𝑛

n=k & k= log n By substitution, we get thus, the complexity is 𝑂 log n 𝑛 𝑂 𝑛 𝑏 The problem consists of a finite set of inputs, 𝑇 𝑛

𝑎𝑡

but its computation time linearly increases. Thus,

𝑛 2 𝑛 𝑇 𝑛 𝑡 𝑛 𝑇 𝑛 𝑡

𝑇 𝑛

Siri

𝑡

𝑂 𝑛 𝑂 𝑛 𝑂 𝑛

Where ignore 𝑡; therefore 𝑇 𝑛

𝑂 𝑛 𝑛 𝑂 𝑛 𝑇 𝑛 𝑎𝑡 𝑏 Where the problem is divided into two parts with the same size. However, the algorithm is infinite. Thus.

𝑛 2 𝑛 𝑇 𝑛 2𝑡 2 𝑛 𝑛 4𝑡 4 𝑛 𝑇 𝑛 4𝑡 𝑛 𝑇 𝑛 4𝑡 𝑇 𝑛

Simsimi

𝑂 𝑛

2𝑡

𝑇 𝑛 𝑇 𝑛

𝑂 𝑛 𝑛

𝑛 2𝑛

2𝑛

𝑂 𝑘𝑛 𝑂 log 𝑛 𝑛

Where k= log n 𝑇 𝑛

𝑂 𝑛 log 𝑛 𝑛 𝑂 𝑛 𝑏 The problem consists of a finite set of inputs, 𝑇 𝑛

  Fig. 3. Showing the time complexity of our proposed chatbot and other known chatbots in the best-case.

Cortana

𝑎𝑡

but computation complexity remains constant ‘n.’

85 | P a g e

Computing Conference 2018 10-12 July 2018 | London, UK  𝑛 2 𝑛 𝑡 2

𝑇 𝑛

𝑡

𝑇 𝑛

field in the future. Chatbots are expected to change the way how business is done by changing the end-user experience and how the companies advertise and make money.

𝑂 𝑛 𝑛

𝑛

.. .. .. .. 𝑛 𝑛 𝑡 1

𝑛

𝑛

𝑡

𝑇 𝑛 𝑇 𝑛

𝑛

𝑛

𝑡

𝑛

𝑛 𝑛

Where ignore 𝑡; therefore, we get 𝑇 𝑛

𝑛

𝑛

n=k & k= log n we get O( log n + n)

𝑛 𝑂 𝑛 𝑏 The problem is divided into two parts with 𝑇 𝑛

𝑎𝑡

different size according to the need of the

Google Now

proposed algorithm. 𝑛 𝑇 𝑛 𝑡 3 𝑛 𝑇 𝑛 𝑡 3 𝑛 𝑇 𝑛 𝑡 3 𝑛 𝑇 𝑛 𝑡 𝑛 𝑇 𝑛 𝑇 𝑛 𝑇 𝑛

VIII.

There is a vast potential for the growth of chatbots. The future looks very promising as well as challenging. Trying to achieve an entirely realistic conversation on an emotional level with a robot is not very easy. But with the advanced AI technologies, better algorithms, and machine learning techniques, it can be achieved Especially when not only the tech giants like Facebook, Microsoft or Google but many other start-up companies are also trying to open their chatting platform to all kinds of robots. Moreover, some companies are hoping to become a foundation of the survival for other services in this domain. The slogan “there is always a chatbot for you” will perhaps become a reality soon.

𝑡 𝑡 𝑡 𝑡 2𝑡

2𝑛 3 2𝑛 3 2𝑛 3 2𝑛 𝑛 2𝑛

ACKNOWLEDGMENT This work was supported in part by the National Natural Science Foundation of China under Grant 61572263, Grant 61502251, Grant 61502243, and Grant 61602263.

𝑂 𝑛 [1]

𝑂 𝑛 𝑛

𝑛

𝑛

𝑛

[2]

[3]

𝑙𝑜𝑔𝑛𝑛 𝑂 𝑛𝑙𝑜𝑔 𝑛

CONCLUSION AND FUTURE WORK

The efficient and utility-based platform for chat is proposed with a low time complexity and better efficiency. Humans interact with chatbots rather than network connections or applications, which is the integration of utilities. As the app market is maturing, chatbots based on the text and voice are expected to inherit the app economy, becoming the new growth point of science and technology

[4] [5]

[6] [7] [8]

REFERENCES Spyrou, Evaggelos, Dimitris Iakovidis, and Phivos Mylonas, eds. Semantic Multimedia Analysis and Processing. CRC Press, 2014. Jacko, Julie A., ed. Human-computer interaction handbook: Fundamentals, evolving technologies, and emerging applications. CRC Press, 2012. Barga, Roger, Valentine Fontama, and Wee Hyong Tok. "Cortana analytics." In Predictive Analytics with Microsoft Azure Machine Learning, pp. 279-283. Apress, 2015. Chen Chaoguo and Liu Rui “Chatbot is the new entrance without only apps,” (2016) Jacko, Julie A., ed. Human-computer interaction handbook: Fundamentals, evolving technologies, and emerging applications. CRC Press, 2012. Sina Technology “I'm smoking kush in front of the police,” unpublished. Bernard Marr “From Big Data to Insights: What Questions Would You Ask Your AI Chat Robot?” Assefi, Mehdi, Guangchi Liu, Mike P. Wittie, and Clemente Izurieta. "An experimental evaluation of Apple’s Siri and Google speech recognition." Proceedings of the 2015 ISCA SEDE (2015). 

86 | P a g e