Socially situated artificial intelligence enables learning from human interaction.

ABSTRACT: Regardless of how much data artificial intelligence agents have available, agents will inevitably encounter previously unseen situations in real-world deployments. Reacting to novel situations by acquiring new information from other people-socially situated learning-is a core faculty of human development. Unfortunately, socially situated learning remains an open challenge for artificial intelligence agents because they must learn how to interact with people to seek out the information that they lack. In this article, we formalize the task of socially situated artificial intelligence-agents that seek out new information through social interactions with people-as a reinforcement learning problem where the agent learns to identify meaningful and informative questions via rewards observed thro

SUBMITTER: Krishna R 

PROVIDER: S-EPMC9522339 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature