Abstract
Problem statement: Artificial intelligence chatbot is a technology that makes interactions between man and machines using natural language possible. From literature, we found out that in general, chatbot are functions like a typical search engine. Although chatbot just produced only one output instead of multiple outputs/results, the basic process flow is the same where each time an input is entered, the new search will be done. Nothing related 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 functionality 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 redesign 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 Diabetes 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 conversation 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.
Highlights
In 1950, mathematician Turing (1950) proposed the question “Can machines think?”
Reviewing the evolving of chatbot technology that paralleling with the evolving of computer science technology, ELIZA stored its knowledge-based data by embedding it right into the code and later came Artificial LinguisticInternet Computer Entity (ALICE) that uses Artificial Intelligence Markup Language (AIML) which is a derivative of Extensible Markup Language or XML (Shawar and Atwell, 2007; Wallace, 2009) to stored the knowledge-based data
The test is in specific domain of knowledge, the relation’s architecture was rather similar, as it is not based on any specific knowledge, but on relations between responses in any chatting conversation
Summary
In 1950, mathematician Turing (1950) proposed the question “Can machines think?”. Since a number of technologies have been emerged in computer science field by the attempt to encounter that particular question that generally founded the field of Artificial Intelligence. One of technology that attempts to visualize an intelligence machine is chatbot or chatter robot that makes interaction between man and machine using natural language possible. The huge breakthrough in chatbot technology came in 1995 where Dr Richard Wallace, an ex-Professor of Carnegie Mellon University combine his background in computer science with his interest in the internet and natural language processing to produce Artificial Linguistic. Reviewing the evolving of chatbot technology that paralleling with the evolving of computer science technology, ELIZA stored its knowledge-based data by embedding it right into the code and later came ALICE that uses Artificial Intelligence Markup Language (AIML) which is a derivative of Extensible Markup Language or XML (Shawar and Atwell, 2007; Wallace, 2009) to stored the knowledge-based data. Developed by Ohno-Machado and Corresponding Author: Abbas Saliimi Lokman, Faculty of Computer Systems and Software Engineering, University Malaysia Pahang, Lebuhraya Tun Razak, 26300 Kuantan, Pahang, Malaysia 1212
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