On Hyper-local Conversational Agents in Urban Settings

Abstract

Conversational agents are increasingly becoming digital partners in our everyday computational experiences. Although rich, and fresh in content, these agents are completely oblivious to users' locality beyond geospatial weather and traffic conditions. In this position statement, we envisage a brand-new class of conversational agents that are hyper-local, embedded deeply in a local neighbourhood, e.g., at urban landmarks - providing rich, purposeful, detail, and in some cases playful information relevant to a neighbourhood. By design, these agents are spatially constrained, and one can only interact with them once she is in close vicinity at street-level granularity. Learning from quantitative (n=1992) and qualitative (n=21) studies, we identify a set of information that these agents must accommodate. Finally, we discuss the technical architecture of this class of conversational agents that leverages covert communication channel, edge AI and on-body devices for offering such hyper-local information access.