Chatbot generation and integration: A review

Abstract: This literature review presents the History, Technology, and Applications of Natural Dialog Systems or simply chatbots. It aims to organize critical information that is a necessary background for further research activity in the field of chatbots. More specifically, while giving the historical evolution, from the generative idea to the present day, we point out possible weaknesses of each stage. After we present a complete categorization system, we analyze the two essential implementation technologies, namely, the pattern matching approach and machine learning. Moreover, we compose a general architectural design that gathers critical details, and we highlight crucial issues to take into account before system design. Furthermore, we present chatbots applications and industrial use cases while we point out the risks of using chatbots and suggest ways to mitigate them. Finally, we conclude by stating our view regarding the direction of technology so that chatbots will become really smart.

Abstract: Conversation Agents also known as Chatbots are considered the next big thing in the modern technological era. Chatbots aim to emulate humans, by mimicking human conversation in the most realistic way possible. There are several methods to generate such intelligent agents. Thus to evaluate the effectiveness of the various approaches, a systematic review of the approaches is required. This paper traces the evolution of chatbots and also elaborates on the impact chatbots have in different industries. Additionally, it also presents a survey of the different modern chatbot methodologies that different researchers have proposed. When analyzing the different approached, this paper has taken into consideration the dataset, input-output, design, development, performance metric, and limitations into consideration. In the end, we've elaborated on the key insights that were obtained pertaining to the crucial factors that influence chatbot design, generation, and development.

Abstract: Today is the era of intelligence in machines. With the advances in Artificial Intelligence, machines have started to impersonate different human traits, a chatbot is the next big thing in the domain of conversational services. A chatbot is a virtual person who is capable to carry out a natural conversation with people. They can include skills that enable them to converse with the humans in audio, visual, or textual formats. Artificial intelligence conversational entities, also called chatbots, conversational agents, or dialogue system, are an excellent example of such machines. Obtaining the right information at the right time and place is the key to effective disaster management. The term "disaster management" encompasses both natural and human-caused disasters. To assist citizens, our project is to create a COVID Assistant to provide the need of up to date information to be available 24 hours. With the growth in the World Wide Web, it is quite intelligible that users are interested in the swift and relatedly correct information for their hunt. A chatbot can be seen as a question-and-answer system in which experts provide knowledge to solicit users. This master thesis is dedicated to discuss COVID Assistant chatbot and explain each component in detail. The design of the proposed chatbot is introduced by its seven components: Ontology, Web Scraping module, DB, State Machine, keyword Extractor, Trained chatbot, and User Interface.

Abstract: Kemajuan teknologi membuat banyak otomatisasi dalam dunia industri, Salah satunya penerapan Chatbot pada industri pendidikan. Dengan teknologi otomatisasi ini, Memudahkan universitas untuk melayani mahasiswa kapanpun waktunya dan dimanapun tempatnya. Kurangnya literasi mahasiswa terhadap fungsi dan penggunaan web kuliah dalam melaksanakan kuliah online menyebabkan banyaknya pertanyaan yang sama secara berulang kepada pihak universitas yang sebenarnya pertanyaan yang sering ditanyakan sudah ditulis dalam daftar pertanyaan yang sering muncul(Faq), seperti: pengumpulan tugas, lupa password , kuliah online, kuliah video conference dan aplikasi web kuliah di android. Dengan menggunakan Chatbot secara otomatis akan menjawab pertanyaan mahasiswa dilaman web kuliah dengan memberikan informasi dan mengarahkan sesuai dengan jawaban pertanyaan. Dalam penelitian ini akan dikembangkan model Chatbot berbasis teks dengan menggunakan algoritma Artificial neural network (ANN). Dataset yang digunakan untuk melakukan pelatihan Chatbot merupakan data pertanyaan sering timbul (Faq) didalam web kuliah yaitu 25 pertanyaan beserta jawaban yang dibagi kedalam 16 label atau kelas. Pengujian dilakukan dengan menggunakan 110 percakapan yang berbeda dengan dataset tetapi mempunyai maksud yang sama. Dari 110 percakapan Chatbot berhasil menjawab dengan tepat sebanyak 107 percakapan dan jawaban salah sebanyak 3 percakapan. Hasil pengujian menunjukkan hasil yang baik yaitu mempunyai akurasi 97,27 % dan kesalahan 2,72 %. Kata Kunci: Chatbot , Artificial Neural Network, ANN, Berbasis teks.

Abstract: Chatbots are a program designed to simulate an intelligent conversation, generally with one or more humans. In the educational field, they are used to act as a teacher, student or student companion and is capable of processing natural language and offering information in a coherent way in real time through a dialogue. It is possible to find various research related to the development and implementation of chatbots such as graduate and postgraduate jobs. In these works the application of an engineering process is evidenced, which in some cases differ from each other, but have activities and good practices in common used for the development of these systems. Therefore, the objective of this research was to define an engineering process for the development of a chatbot based on good practices identified in research carried out. Thus, establishing the processes, activities and artifacts to be carried out for the development of a chatbot that allows the evaluation and training of students in a given subject.

Abstract: I propose to consider the question, “Can machines think?”♣ This should begin with definitions of the meaning of the terms “machine” and “think”. The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll.