Modelling Mutually Interactive Fictional Character Conversational Agents

This talk was originally presented by Thomas Winters on the 6th of November 2019 at BNAIC19, the 31th Benelux Conference on Artificial Intelligence. The focus on the talk is about modelling interactive Twitterbots. These are based on the Belgian children TV show Samson & Gert, to create the Samsonbots. We also show and release our new probabilistic context-free grammar modeling tool called Babbly. Paper abstract: Conversational agents, such as chatbots and virtual assistants, are typically modelled to have a broad, generic personality, which they employ in their communication with single human beings. However, by framing a conversational agent as existing fictional characters, humans can imagine a shallow agent to have a larger personality than without this framing. Using multiple such agents allows for conversational interactions that help construct stories with or without human intervention, leading to multi-agent human-computer interactive story telling. In this paper, we model six semi-independent Twitterbots based on fictional characters based on the Belgian children’s TV show Samson & Gert, which are mutually interactive with each other as well as with other Twitter users. To achieve this, we first introduce a new language for modelling generative weighted context-free grammars called Babbly and a new framework for easily specifying complex Twitterbot behaviour. We found that these bots were not only well received by users, but also created lots of interesting, unexpected positive interactions. Using fictional characters as framing for conversational agents can thus help achieving interesting personalities and shows potential in interactive computational story telling.