Implicit Communication of Actionable Information in Human-AI teams

Claire Liang, Cornell University


Humans expect their collaborators to look beyond the explicit interpretation of their words. Implicature is a common form of implicit communication that arises in natural language discourse when an utterance leverages context to imply information beyond what the words literally convey. Whereas computational methods have been proposed for interpreting and using different forms of implicature, its role in human and artificial agent collaboration has not yet been explored in a concrete domain. The results of this paper provide insights to how artificial agents should be structured to facilitate natural and efficient communication of actionable information with humans. We investigated implicature by implementing two strategies for playing Hanabi, a cooperative card game that relies heavily on communication of actionable implicit information to achieve a shared goal. In a user study with 904 completed games and 246 completed surveys, human players randomly paired with an implicature AI are 71% more likely to think their partner is human than players paired with a non-implicature AI. These teams demonstrated game performance similar to other state of the art approaches