  Conversational systems typically focus on functional tasks such as scheduling
appointments or creating todo lists. Instead we design and evaluate SlugBot
(SB), one of 8 semifinalists in the 2018 AlexaPrize, whose goal is to support
casual open-domain social inter-action. This novel application requires both
broad topic coverage and engaging interactive skills. We developed a new
technical approach to meet this demanding situation by crowd-sourcing novel
content and introducing playful conversational strategies based on storytelling
and games. We collected over 10,000 conversations during August 2018 as part of
the Alexa Prize competition. We also conducted an in-lab follow-up qualitative
evaluation. Over-all users found SB moderately engaging; conversations averaged
3.6 minutes and involved 26 user turns. However, users reacted very differently
to different conversation subtypes. Storytelling and games were evaluated
positively; these were seen as entertaining with predictable interactive
structure. They also led users to impute personality and intelligence to SB. In
contrast, search and general Chit-Chat induced coverage problems; here users
found it hard to infer what topics SB could understand, with these
conversations seen as being too system-driven. Theoretical and design
implications suggest a move away from conversational systems that simply
provide factual information. Future systems should be designed to have their
own opinions with personal stories to share, and SB provides an example of how
we might achieve this.
