Development of Artificial Intelligence Based Chat bot for Project Assistance in Automotive Applications

Authors

  • Dr. A. Natarajan
  • T. Bogaraj

DOI:

https://doi.org/10.46243/jst.2022.v7.i01.pp62-71

Keywords:

Artificial Intelligence, Chatbot, Chatterbot, Project Assistance, Answering System

Abstract

Improvements in Technology helps human to lead life easier when compared to ancient times. With new developments in software technology, apps and software tools are become part of life. Chatbots that are developing

for many applications will find applications in our daily lives also for many personal usages. Even though Chabot

concept is developing one, it is being rapidly adapted for many applications in various areas because this makes the work simpler and easier for the customer. Chatbots are artificial intelligence based tool that chat with one like a person replying from the other side. Project assistance Chatbot is an interactive tool that contains the knowledge base of a particular project or process. The Chatbot will be trained with the data of the particular project. The Chatbot will be able to identify the keywords given by the user, match it with the knowledge that has been fed to it. Finally, it will give the result to the user by logically connecting all the obtained results. Sequence to sequence model has been used to build this Chatbot, which uses encoder and decoder arrangement. The Chatbot model has been implemented with two sets of Recurrent Neural Networks (RNN) using the software Python TensorFlow. Sequence of symbols is encoded by one RNN into fixed-length vector representation and the vectors are decoded into sequence of symbols by the second RNN. The Chatbot has been trained with volume of data and it responds to the user queries.

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Published

2022-01-11

How to Cite

Dr. A. Natarajan, & T. Bogaraj. (2022). Development of Artificial Intelligence Based Chat bot for Project Assistance in Automotive Applications. Journal of Science & Technology (JST), 7(1), 62–71. https://doi.org/10.46243/jst.2022.v7.i01.pp62-71

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