Implicit Communication of Actionable Information in Human-AI teams
By Claire Liang, Julia Proft, Erik Andersen, Ross A. Knepper. 2019
94 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
How researchers see this paper
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In Artificial Intelligence
- Jakob N. Foerster
- Sergey Levine
- Yoshua Bengio
- Pieter Abbeel
- Mohit Bansal
- Hengyuan Hu
- Ruslan Salakhutdinov
- Yuxi Li
Citations in the corpus
- Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning (2019, background): “, 2019) and even to extend to human-AI collaboration (Liang et al., 2019).”
- The Hanabi Challenge: A New Frontier for AI Research (2019, background): “Liang and colleagues [49] compared the performance of artificial agents that implicate additional meaning through their hints with agents that simply hint to minimise entropy, and found that humans were more likely to believe the implicature-based agent was also a human.”
- Executing Instructions in Situated Collaborative Interactions (2019, background): “C ERE - AL B AR also provides opportunities to study pragmatic reasoning for language understanding (Andreas and Klein, 2016; Fried et al., 2018; Liang et al., 2019).”