Добірка наукової літератури з теми "Chat-bot generation"

Conversational agents, also known as chatbots, are automated systems for engaging in two-way dialogue with human users. Nowadays the use of Chatbots is very popular in a large scale of applications especially in systems that provide an intelligence support to the user. In fact, to speed up the assistance, in many cases, these systems are equipped with Chatbots that can interpret the user questions and provide the right answers, in a fast and correct way. Chatbots typically provide a text-based user interface, allowing the user to type commands and receive text as well as text to speech response. When chat bot technology is integrated with popular web services it can be utilized securely by an even larger audience. The student information chat bot will analyse user’s queries and understand user’s message for appropriate response generation. This System will be a web application which provides answer to the query of the student very effectively. Students just have to put their query to the bot which is used for chatting. The system will use the algorithms to give appropriate answers to the user. If the answer is found invalid, then there is an option to report to admin so that the users query will be satisfied. These invalid questions can be deleted or modified by the admin of the system and an appropriate answer can be embedded in the database. The student will not have to go to the college for enquiring something. Student can use the chat bot to get the answers to their queries. Students can use this web based system for making enquiries at any point of time. This system may help students to stay updated with the college activities.

Excellent service is the best form of service to consumers to feel satisfied. The purpose of excellent service is that consumers or prospective consumers feel underserved and in terms of agencies or companies will increase consumer confidence. Muhammadiyah University Purworejo (UMP) as an educational institution has prospective consumers, namely the millennial generation. The habit of the millennial generation in accessing information is by digitally searching. To access more detailed information, they are accustomed to asking through the texting method via chat. Therefore, the University needs to provide a texting application that can answer questions anytime and with a short reply time.The chat application developed in the form of chatbots.The chat-bot application development begins with studying general questions that are often asked by prospective students. The combination of question words and keywords from each question. From the keywords that are prepared an automatic answer that contains information that the prospective student wants to access. In addition, in each answer a key for information related information will be provided. The results of the design and development of the Chatbot application, namely the implementation of Chatbot can be used as a substitute for the role of humans in providing excellent service. This chatbot can provide quick and complete answers. In terms of the excellent service factor, this Chatbot can fulfill the attitude, attention, action, ability and accountability factors.

Information at finger tips” says the agenda of the work very much clearly. Basic idea behind this work is to provide an easy and most suitable way for users to find the information. Today’s generation is keen to gain knowledge, hence this platform will play an important role to help them and to act as a bridge that connects very easily to the required information is our main motive. We’ve considered the issue of Shopping. As there is increasing growth in the cities, the resources have also increased. We can say that for one thing there are multiple options present nowadays. Every citizen wants to be smart buyer where he can make his profit in terms of product quality and great discounts also. To tackle this issue we are building this system where they can search any related information. The operations will be performed in Python3. We will be providing an interface where the information can be accessed. To advance it one step further we will be using a chat bot for any assistance.

The hassle of deciding on a travel destination is often overlooked by travel websites. Travellers, a lot of times, do not have a clear idea of where they want to travel to. We aim on solving this problem by introducing a chat bot system that can recommend travel destinations based on minimal information from the traveller. Another interesting feature of the project is its itinerary generator. The system aims on providing a human-like user experience through the use of a chatbot interface. The interface interacts with the user to retrieve information about the user’s details like travel date, number of children and adults travelling and the budget of travelling. The user’s budget will be the main focus of this application as we want to give the end-user the best travel experience based on their particular budget. The recommender also takes other external factors such as the season, previous traveller experiences and weather into consideration. Incorporating these factors ensure that the most optimal destination is recommended to the user. As per the recommendation, the user can opt to get several itineraries to choose from. The itinerary generator also takes several external factors into consideration when generating an itinerary. The choice of itineraries vary in the choice of places and in activities according to budget and other external factors.

The hassle of deciding on a travel destination is often overlooked by travel websites. Travellers, a lot of times, do not have a clear idea of where they want to travel to. We aim on solving this problem by introducing a chat bot system that can recommend travel destinations based on minimal information from the traveller. Another interesting feature of the project is its itinerary generator. The system aims on providing a human-like user experience through the use of a chatbot interface. The interface interacts with the user to retrieve information about the user’s details like travel date, number of children and adults travelling and the budget of travelling. The user’s budget will be the main focus of this application as we want to give the end-user the best travel experience based on their particular budget. The recommender also takes other external factors such as the season, previous traveller experiences and weather into consideration. Incorporating these factors ensure that the most optimal destination is recommended to the user. As per the recommendation, the user can opt to get several itineraries to choose from. The itinerary generator also takes several external factors into consideration when generating an itinerary. The choice of itineraries vary in the choice of places and in activities according to budget and other external factors.

In the early days of Artificial Intelligence, it was observed that tasks which humans consider ‘natural’ and ‘commonplace’, such as Natural Language Understanding, Natural Language Generation and Vision were the most difficult task to carry over to computers. Nevertheless, attempts to crack the proverbial NLP nut were made, initially with methods that fall under ‘Symbolic NLP’. One of the products of this era was ELIZA. At present the most promising forays into the world of NLP are provided by ‘Neural NLP’, which uses Representation Learning and Deep Neural networks to model, understand and generate natural language. In the present paper the authors tried to develop a Conversational Intelligent Chatbot, a program that can chat with a user about any conceivable topic, without having domain-specific knowledge programmed into it. This is a challenging task, as it involves both ‘Natural Language Understanding’ (the task of converting natural language user input into representations that a machine can understand) and subsequently ‘Natural Language Generation’ (the task of generating an appropriate response to the user input in natural language). Several approaches exist for building conversational chatbots. In the present paper, two models have been used and their performance has been compared and contrasted. The first model is purely generative and uses a Transformer-based architecture. The second model is retrieval-based, and uses Deep Neural Networks.

Increasingly, online information is produced by AI-based writing systems such as the Washington Post newswriting bot, Heliograf; Narrative Science's report writing app, Quill; Persado’s marketing copy app, and chat and twitterbots too numerous to name. What are the implications—for us, for our society—as we enter the age of AI writing systems, an era moving rapidly toward a world in which writing is produced mostly by machines rather than humans? What are the ethical implications of these developments? Our research on AI-based writing systems is based on (1) critical analysis of the systems themselves and on (2) interviews with the designers and users of these systems. Our analysis draws from the field of machine ethics, as well as communication/language theory. In our presentation we will discuss several AI-writing systems (e.g., GPT-2, Persado, Quill, Google Compose), focusing especially on the language model or "informational framework" (Russo, 2018) that supports their operation. What our research reveals, so far, is that, not surprisingly, these systems are more effective (and ethical) handling well-defined tasks in bounded spaces, with well-established genre conventions, clearly identified audience needs and expectations, and predictable interaction scripts. What we also see is that too often the language models employed are based on a reductive, formalist model of text generation. AI-writing system designers need to move beyond formalist, linear input/output models to more complex social models that account for the broader contexts, including the ethical codes, in which communications arise and circulate.

The importance of Chatbots in marketing, particularly in employment or training progressions and maintenance for definite promotional goals, tools as well as approaches was examined in this study. The study seeks to ascertain the opportunities associated with the usage of chatbots in marketing, with specific emphasis on its influence in the process of Human-to-Machine communications, and find out the extent to which Chatbots could be effectively use to examine competitive companies or brands. The researcher explores how chatbot can interact with users using Facebook messenger and investigate the impact and usefulness of digital marketing communications tools on organizational profit maximization, using a real estate business. The study focuses on three sub-processes in the chatbot design, which includes writing handling, language acceptance, as well as reaction generation. Additionally, the survey was piloted with arrangement of chatbot assessment methods and their examination in relation to chatbot categories as well as three central appraisal schemes, which include content estimation, user gratification, and chat function. Findings of the study established that Chatbots could be effectively used to enable companies or brands intensify organizational profit maximization and proved that the limitations of the human agents have been taken over by this automatic Bot, which have been trained to act like human, give responses to customers’ requests and even suggest responses to users. It recommends that every organization marketing teams should acquire innovative communication approaches about how to preserve and advance enduring relations with standing consumers as well as how to get the attention of potential consumers.

Кваліфікаційна робота включає пояснювальну записку (с. 72, рис. 58, табл. 2, додатки). Об’єкт розробки – створення мови та компілятора для генерування тексту чат-ботів мовою JavaScript, яка буде зручна як для людей, які не пов’язані з інформаційними технологіями, так і навпаки. Головна мета мови – спростити та прискорити створення чат-ботів без додаткових знань мов програмування чи архітектурних рішень, які використовуються під час проєктування програмного забезпечення. Мова для генерування чат-ботів має наступні можливості: • генерування чат-ботів для вебсайтів; • генерування чат-ботів для соціальної мережі «Telegram». Компілятор мови має наступні можливості: • швидко компілювати та аналізувати код, написаний мовою, яка представлена в цьому дипломному проєкті. Генерований код має наступні властивості: • легкий у розумінні; • зручний у модифікації. У процесі розробки були використанні наступні мови програмування: • Common Lisp з використанням наступних бібліотек: «alexandria» та «anaphora» – яка додає додаткову функціональність до мови для зручної розробки, «lisp-unit» – для створення unit-тестів, «cl-ppcre» – для роботи з регулярними виразами, «unix-opts» – для обробки опцій компілятора, які надходять з операційної системи, «yason» – для роботи з JSON-об’єктами, «osicat» – для роботи із символічними посиланнями у unix-подібних операційних системах; • JavaScript з використанням наступних бібліотек: «expressjs» – для створення вебсерверу з використанням HTTP/HTTPS протоколів, «telegrafjs» – для роботи з API соціальної мережі «Telegram», «lodash» – яка додає додаткову функціональність до мови для зручної розробки; Середовища розробки: • WebStorm; • Emacs з додатком Slime. Додаткові програми: • Sbcl для компіляції файлів на мові Common Lisp; • NodeJS для інтерпретації файлів на мові JavaScript; • Було також використано додаток NPM для автоматичної збірки проєкту на мові JavaScript, Apple Safari – веб-браузер для налагодження чат-ботів, які були генеровані для вебсайтів та соціальна мережа «Telegram» для налагодження роботи чат-ботів, які були генеровані для цієї платформи. В ході виконання дипломного проєкту: • розроблено архітектуру системи; • проведений аналіз існуючих рішень; • розроблена мова та компілятор для генерації чат-ботів мовою JavaScript для вебсайту або соціальної мережі. Використання цієї мови та компілятора дозволять прискорити час розробки чат-ботів для різних платформ та надати можливість зосередитися на реалізації основної задачі чат-бота.
Qualification work includes explanatory note (p. 72, fig. 58, tables 2, applications). The object of development is the creation language and compiler for chat-bot generation in JavaScript language, which will be comfortable for people who not affiliated with information technology and vice versa. The main goal of the language to simplify and to accelerate chatbots creation without additional knowledges of programming languages or architecture solutions which using during software development. Language for chatbot generation have the next features: • the ability of chatbot generation for websites. • the ability of chatbot generation of social network “Telegram”; Compiler of language have the next features: • the ability of quick code analyzing and compilation in the language whose introduced in this graduation work; Generated code has the next features: • easy to understanding; • comfortable to modification. The development process used the next programming languages: • Common Lisp with using the next libraries: “alexandria” and “anaphora” which provides additional functionality for language, “lisp-unit” for writing unit-tests, “cl-ppcre” for working with regular expressions, “unix-opts” for handling options of the compiler that passed from operating system, “yason” for working with JSON-objects, “osicat” for working with symbolic links in unix-like operating systems; • JavaScript with using the next libraries: “expressjs” for creation webserver using HTTP/HTTPS protocols, “telegrafjs” for working with API of social network “Telegram”, “lodash” which provides additional functionality for language. Development environments: • WebStorm; • Emacs with Slime extension. Additional programs: • SBCL for compiling files in Common Lisp; • NodeJS for compiling files in JavaScript language; • NPM for auto building projects in JavaScript language; • Apple Safari web-browser for debugging chatbots that generated for websites; • social network “Telegram” for debugging chatbots that generated for this platform. During the implementation of the graduation work: • developed architecture of project; • analysis existent solutions; • developed language and compiler for chat-bots generation in JavaScript language for websites and social networks. Using this language and compiler can accelerate development time of different platforms, and provide focus on the main functionality of chatbot.

Recently a new method for training generative neural networks called Generative Adversarial Networks (GAN) has shown great results in the computer vision domain and shown potential in other generative machine learning tasks as well. GAN training is an adversarial training method where two neural networks compete and attempt to outperform each other, and in the process they both learn. In this thesis the effectiveness of GAN training is tested on conversational agents also called chat bots. To test this, current state-of-the-art training methods such as Maximum Likelihood Estimation (MLE) models are compared with GAN method trained models. Model performance was measured by closeness of the model distribution from the target distribution after training. This thesis shows that the GAN method performs worse the MLE in some scenarios but can outperform MLE in some cases.
Nyligen har en ny metod för att träna generativa neurala nätverk kallad Generative Adversarial Networks (GAN) visat bra resultat inom datorseendedomänen och visat potential inom andra maskininlärningsområden också GAN-träning är en träningsmetod där två neurala nätverk tävlar och försöker överträffa varandra, och i processen lär sig båda. I detta examensarbete har effektiviteten av GAN-träning testats på konversationsagenter, som också kallas Chat bots. För att testa det här jämfördes modeller tränade med nuvarande state-of- the-art träningsmetoder, så som Maximum likelihood-metoden (ML), med GAN-tränade modeller. Modellernas prestation mättes genom distans från modelldistribution till måldistribution efter träning. Det här examensarbetet visar att GAN-metoden presterar sämre än ML-metoden i vissa scenarier men kan överträffa ML i vissa fall.