Chatbot that employs a limited set of rules
A chatbot refers to a computer software that has the ability to converse with human beings by use of intelligence in texting platforms. This can be message communication, a spoken communication as well as non-verbal conversation. Chatbot can operate on local personal computers as well as phones although most of the times it is accessed via the internet. The chatbot is generally viewed as an engaging program entity that human beings can converse with. It can be inspiring, interesting as well as intriguing and it can be accessed at any place, from earlier HTML pages to current enhanced social networking, websites as well as from quality computers to a modern smartphone gadget [1]. Chatbot converses in various main languages, their Natural Language Processing (NLP) professionalism from highly, poor to very bright intelligent, funny as well as helpful..
Over the previous years, texting programs have become more common compared to networking sites. Currently, individuals are making use of texting programs which include Facebook Messenger, Viber, Skype, Slack, Telegram among others. This is resulting in the opening of businesses on texting platforms which leads to extreme interaction with clients on their products. Therefore, for interaction with many users using texting platforms, the businesses need to embrace chatbot development that can communicate like a human. Chatbot has various categories which include a limited set of rules as well as machine learning.
Chatbots that make use of a limited set of rules are restricted to the set rules of messaging or commands and they are capable to react only to those messages or commands [2]. If a client queries anything different other than the set of messages or commands that are set to the bot, it would be able to react as expected for it is not able to understand or it lacks skills on what client asked. These kinds of bots are good compared to the other bots.
The chatbot that deploy machine learning functions by use of artificial intelligence. The client does not need to be more specific while conversing with a bot for it can perceive the natural language, not only the defined commands [3]. This type of bot grows smarter as it learns from previous talks it had with individuals. The following is an example that highlights how it operates. Here is a sample conversation between a chatbot and a human:
Human: “I need a food from your Moven Hotel.”
Chatbot that uses machine learning
Bot: “Sure! Which food do you want to order?’
Human: “Hot Coffee.”
Bot: “Well! Contacting the Waiter.”
Literature Review
This literature review entails a review of the related System to chatbot system on computing devices. The chatbot systems are not only used in firms but also in colleges to satisfy the needs of the students without the need of traveling miles to the school [4]. The chatbot system is an online application for responding to the queries of the user by either through a texting platform or conversation on behalf of the company help desk. For instance, chatbot is offered by colleges to update students on admission, activities among others.
ELIZA
ELIZA is a popular piece of a program developed in the year 1966 by Joseph Weinbaum to emulate psychotherapist. It poses queries to humans being subjects with respect to their input and has the capacity to carry out this without sophisticated natural language processing methods. Alternatively, it scans what is entered for common keywords and converts it into a fresh query, in respect with the set of rules as illustrated in its scripts. However, it is possible for a sufficiently enhanced form of ELIZA to output the response which if sophisticated creates a natural language processing that is being deployed and that the personal computer can literally recognize the input it accepts [5]. At the architecture of any application of ELIZA lies the script, this script refers to keywords, disintegration as well as assembly laws for every keyword, pre and post-processing metonym for advanced extensibility. Theoretically, ELIZA preserves its scripts as a tree, every keyword brings about several disintegration rules. These policies describe how an input that resembles that keyword should be disintegrated for advanced processing [5]. Consecutively, every disintegration rules progenerate various reassembly policies that state how a disintegrated input should be recreated into a feedback.
Keywords are arguments used to establish the aim of the sentence. The essential opinion behind this is that every sentence has got a particular target. That aim necessitates the deployment of given words, for instance, the statement of desire obligation the deployment of words such as want as well as need. This association goes all ways. Therefore, the deployment of given words such as need or want, show that the client needs something. The word-intention association permits the author of ELIZA to derive a set of efficient feedback for the correct idea if a keyword is established.
Literature Review
It is the web application that draws weather forecast for a particular town from yahoo weather API. It inquires yahoo weather API by use of curl and gets the page with a weather predicting data for a particular town. The forecast data are fed as JSON object. To view the weather data, the responded JSON object is defined and retrieves the relevant data that is temperature as well as condition. The feedback is built correctly with temperature as well as a condition of a given place [6]. When the program is run, the weather details are shown.
A client who joins the chat group can begin the conversation with other group users. When a client begins a new thread to an existing chat, many people will be alerted through email as long as they have their email set correctly. In regard to the thread of the message, apart from the commenter, owner of the group, individual who initiated the thread as well as everyone who has texted on the thread except the users of the bot would be alerted through email [7]. The users of the bot who have reacted on the thread will be alerted by clicking URL with the earlier post as an element. This depicts how the bot communication implementation is carried out. In order to communicate with other users, you need to create a bot account with a specific token as well as callback URLs. For you to converse in a group, it is compulsory to state bot name with @ in the comment. It has the ability to get bot name using normal expression and confirms whether the needed bot is the bot client and also confirms whether it has got it in the group.
The main aim of this research was to test the academic as well as industry literature to offer an extensive review of quality aspect for chatbot as well as conversational agents and establish correct quality assurance techniques. To achieve this, documentation on quality in systems of chatbot as well as conventional agents were established via systematic studies in JSTOR, Google Scholar as well as EBSCO Host from the year 1990 to the year 2017 [8]. The search words that were deployed were embodied conventional agents, chatbots as well as quality assurance in different combinations. Articles were drafted from the domains of technology, engineering, communications, psychology as well as anthropology. The selection of researches was done in respect of three criteria. To begin with, academic sources were emphasized and complemented only by firm documentation from the year 2016 to the year 2017. Documentations were chosen if they constituted at least a single search term in the head as well as abstract to guarantee the significance of data gathering. Only documentation was chosen that possessed quality as the main or influential element of the study. Lastly, great technical papers aimed at programming as well as engineering components of chatbots, incorporating advancing the quality of speech identification, were excluded.
Overview and Analysis of the Related Systems
The first search produced a sampling structure of 7,340 papers that was more refined by adding search terms for testing, investigation, metrics as well as quality metrics. Current articles from the year 2016 to 2017 were examined, followed by papers between 2013 as well as 2015 then followed by the year 2007 to 2012. The choosing of time aspect was made to reduce the number of researches for evaluation to less than 300 conference papers. 36 scholar papers were established to be significant to the target of this article [9]. They were reinforced with 10 articles originating from industry magazines. Past this, only 7 articles were established for the second part of the research aiming at a quality guarantee and all were deployed in the study. We drew quality element from every 32 papers as well as ten articles and joined them in respect of similarity. After two or three emphases we realized that in common, they were set with the ISO 9241 factor of usability, the effectiveness, satisfaction as well as the efficiency with which certain clients attain given goals in specified environments [10]. To be specific, effectiveness means nearness to the correctness as well as completeness with which particular clients attain their targets while efficiency means to how well tools are deployed to attain those objectives.
Proposed Research
Research Title
Chatbot system.
Traditionally, systems of chatbot were not known to individuals who were not concerned with technology and with the presence of chatbot system, it is inaccurate in justifying the solution or answer. For instance, where a college lacks the chatbot system, the students need to spend a lot of time and resources to come to the school and bring questions to the help desk of the college which is a cumbersome process. In such a situation the process utilizes a lot of time, resources and can result in a communication gap between the clients and the organization or institution. Therefore, it is necessary to develop a chatbot system that allows users to get desired responses at their comforts.
The aim of the Research
- To analyze the existing manual helpdesk system where you are required to avail in person in order to pass your grievances and get the desired response which consumes a lot of time as well as resources.
- To establish the challenges of the existing Chatbot systems. For example, chatbot systems with set rules cannot respond to anything outside the defined commands [11]. For instance, syntactic indifference is the problem of these applications to understand and include the syntactic structure of processing of the input of the user as well as comprehension.
- To design and develop the Chatbot system which is smarter for the efficient provision of services.
- To test and validate the Chatbot system.
The chatbot system will be designed by making use of an algorithm which analyzes the queries of the clients as well as understanding the message of the user in an appropriate manner. This system will be a web-based application. In order to respond to the questions of the users, the clients need just to query via the chatbot that is meant for chatting [12[. The users can converse by use of any format for there is no defined format for clients to adhere. The chatbot system will employ inbuilt artificial intelligence to respond to query and the answers will be correct in respect of what the user asks. If the answer in place is not correct or is invalid, the client is expected to click the button with invalid answer field in the user interface in order to alert the admin on the invalid answer then the admin can look at the incorrect answer via portal through signing in. The chatbot system permits admin to erase the invalid answer and replace it with the particular answer of that specific query [13]. The user can ask any activity through the system like upcoming events implying that the client does need to travel to the institution or organization. The chatbot system is capable of analyzing queries and react to the client just like conversation between persons. This is aided by the use of artificial intelligence. The chatbot system responds through constructive Graphical user display that depicts that whether a true individual is conversing to the client. The client can question any activity through online with the aid of a web-based application and this assists the users to be updated on the ongoing activities.
ELIZA
Conclusion
A Chatbot is a new software in the market as discussed above and by developing a more enhanced chatbot system, its adoption will be supported. The proposed chatbot will make conversations with humans easier as it is aimed at offering information as well as finishing operations for humans they relate with. This is because the group conversation chatbot permits the client to create an account and get a notification via email, which is an improvement. In addition, the Test Weather Bot gives information on the weather conditions whenever the client queries which will advance production or economic growth. I focus to advance the chatbot system that aims at assisting individuals to carry out their work as well as interacting with machines by use of natural language or using commands. The future chatbot system will be capable of holding previous communications and acquire from them on what to respond to new ones.
References
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[2] Augello, A., Pilato, G., Machi, A. and Gaglio, S. An approach to enhance chatbot semantic power and maintainability: experiences within the FRASI project. In 2012
IEEE Sixth International Conference on Semantic Computing (pp. 186-193). IEEE, 2012.
[3]Da Palma, W.V., Mandalia, B.D., Moore, V.S. and Nusbickel, W.L., Nuance Communications Inc. Dialect translator for a speech application environment extended for interactive text exchanges. U.S. Patent 8,204,182, 2012.
[4]Bradeško, L. and Mladeni?, D.. A survey of chatbot systems through a loebner prize competition. In Proceedings of Slovenian Language Technologies Society Eighth Conference of Language Technologies (pp. 34-37), 2012.
[5]Dale, R.. The return of the chatbots. Natural Language Engineering, 22(5), pp.811-817, 2016.
[6] Duan, X.. Chatbot system and method with interactive chat log. U.S. Patent Application 13/661,045, 2014.
[7]Rosser, R.J. and Sturges, S. Response generator for mimicking human-computer natural language conversation. U.S. Patent 7,783,486, 2010 .
[8]Edoja, D. Method, system and program for analytics data delivering. U.S. Patent Application 13/526,530, 2012.
[9]FitzGerald, C.W. and Shaffer, S., Cisco Technology Inc. Interactive text communication system. U.S. Patent 7,797,387, 2010.
[10] Marciel, K.K., Saiman, L., Quittell, L.M., Dawkins, K. and Quittner, A.L.. Cell phone
intervention to improve adherence: cystic fibrosis care team, patient, and parent perspectives. Pediatric pulmonology, 45(2), pp.157-164, 2010.
[11]Pichponreay, L., Kim, J.H., Choi, C.H., Lee, K.H. and Cho, W.S. Smart answeringchatbot based on OCR and overgenerating transformations and ranking. In Ubiquitous , 2016.
[12]Future Networks (ICUFN),.Eighth International Conference on (pp. 1002-1005).IEEE, 2016.
[13]Takeuchi, Y. and Hoshino, A., Honda Motor Co Ltd. Chat information service system.U.S. Patent 7,711,569 , 2010.
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