Conversational systems or chatterbots converse/chat by learning from their interactions with users. To do this the systems must have an adaptive knowledge base that can be updated by the systems themselves. RONE is a tele-text based conversational system. RONE’s knowledge base is built using SQL and accessed using the main Java application. Additionally, RONE uses conjunctions and prepositions as markers to expedite the dissemination and storage of information which helps him learn. In this paper, we describe the approach RONE uses to break up new information for learning purposes - the principle technique introduced here being the storage of information in a format to answer all the possible questions directly without inference. We also look at other conversation based learning approaches and their limitations. Further, we compare RONE’s performance against some contemporary conversational systems and provide evidence of the relative superior informational accuracy of RONE’s responses to user interrogation. RONE’s better performance is noteworthy because it is relative to systems which are Loebner Prize medal winners.
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