Inferring capabilities of intelligent agents from their external traits

We investigate the usability of humanlike agent-based interfaces for interactive advice-giving systems. In an experiment with a travel advisory system, we manipulate the “humanlikeness” of the agent interface. We demonstrate that users of the more humanlike agents try to exploit capabilities that were not signaled by the system. This severely reduces the usability of systems that look human but lack humanlike capabilities (overestimation effect). We explain this effect by showing that users of humanlike agents form anthropomorphic beliefs (a user's “mental model”) about the system: They act humanlike towards the system and try to exploit typical humanlike capabilities they believe the system possesses. Furthermore, we demonstrate that the mental model users form of an agent-based system is inherently integrated (as opposed to the compositional mental model they form of conventional interfaces): Cues provided by the system do not instill user responses in a one-to-one matter but are instead integrated into a single mental model.