Extension and Prerequisite: An Algorithm to Enable Relations Between Responses in Chatbot Technology

TLDR: This research is focused on enabling chatbot to become a search engine that can process the next search with the relation to the previous search output, and using the relational database model approach to design and incorporate the algorithm of Extension and Prerequisite.

Abstract: Problem statement: Artificial intelligence chatbot is a technology th at makes interactions between man and machines using natural language possible. From literature, we found out that in general, chatbot are functions like a typical searc h engine. Although chatbot just produced only one output instead of multiple outputs/results, the bas ic process flow is the same where each time an inpu t is entered, the new search will be done. Nothing re lated to previous output. This research is focused on enabling chatbot to become a search engine that can process the next search with the relation to the previous search output. In chatbot context, this fu nctionality will enhance the capability of chatbot' s input processing. Approach: In attempt to augment the traditional mechanism of chatbot processes, we used the relational database model approach to r edesign the architecture of chatbot in a whole as well as incorporated the algorithm of Extension and Prerequisite (our proposed algorithm). By using this design, we had developed and tested Virtual Di abetes physician (ViDi), a web-based chatbot that function in specific domain of Diabetes education. Results: Extension and prerequisite enabled relations between responses that significantly make it easier for user to chat with chatbot using the same approach as chatting with an actual human. Chatbot can give different responses from the same input given by user according to current conversati on issue. Conclusion: Extension and prerequisite makes chatting with chatbot becomes more likely as chatting with an actual human prior to the relations between responses that produce a response related to the current conversation issue.