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Learning from Interaction: An Intelligent Networked-based Human-bot and Bot-bot Chatbot System

This idea for a chatbot that actively learnt through interaction was developed for a final year project whilst studying for a Bachelor’s Degree in Computer Science from Aston University (Birmingham, UK). The work proved effective and as such a paper was written for UKCI2018 (18th Annual UK Workshop on Computational Intelligence). The work will also be published in Springer’s Advances in Intelligent Systems and Computing.

Authors – Jordan J. Bird, Diego R. Faria, Anikó Ekárt
Aston Lab for Intelligent Collectives Engineering (ALICE)
School of Engineering and Applied Science, Aston University, UK.

Abstract – In this paper we propose an approach to a chatbot software that is able to learn from interaction via text messaging between human-bot and bot-bot. The bot listens to a user and decides whether or not it knows how to reply to the message accurately based on current knowledge, otherwise it will set about to learn a meaningful response to the message through pattern matching based on its previous experience. Similar methods are used to detect offensive messages, and are proved to be effective at overcoming the issues that other chatbots have experienced in the open domain. A philosophy of giving preference to too much censorship rather than too little is employed given the failure of Microsoft Tay. In this work, a layered approach is devised to conduct each process, and leave the architecture open to improvement with more advanced methods in the future. Preliminary results show an improvement over time in which the bot learns more responses. A novel approach of message simplification is added to the bot’s architecture, the results suggest that the algorithm has a substantial improvement on the bot’s conversational performance at a factor of three.

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