Human-like “agents” or “tools”?: Exploring the implicature-of-quantity in HAI

Abstract

A quantitative implicature task was used to investigate whether humans believe that robots can make human-like inferences. As quantifier terms, such as “little” and “much,” do not specify an exact amount, the actual quantity must be inferred from various pieces of information. The participants (N = 24) had to encounter either 100% perfectly-controllable-robots or randomly-controllable-robots. The participants then determined the exact amount of energy to charge to each vehicle depending on the initial-state of the vehicle and the agent’s request. The results indicated that the participants’ estimates of the amounts for randomly-controlled-robots were similar to those for human agents. However, participants with experience in perfectly-controlled-robots estimated a lower amount when the request was labeled as “little,” indicating their estimation was based on literal interpretation. People seemed to predict that perfectly-controlled-robots would not use human-like inference, implying that perfectly-controlled-robots can be assumed to be just “tools” not human-like “agents.”