  When humans cooperate, they frequently coordinate their activity through both
verbal communication and non-verbal actions, using this information to infer a
shared goal and plan. How can we model this inferential ability? In this paper,
we introduce a model of a cooperative team where one agent, the principal, may
communicate natural language instructions about their shared plan to another
agent, the assistant, using GPT-3 as a likelihood function for instruction
utterances. We then show how a third person observer can infer the team's goal
via multi-modal Bayesian inverse planning from actions and instructions,
computing the posterior distribution over goals under the assumption that
agents will act and communicate rationally to achieve them. We evaluate this
approach by comparing it with human goal inferences in a multi-agent gridworld,
finding that our model's inferences closely correlate with human judgments (R =
0.96). When compared to inference from actions alone, we also find that
instructions lead to more rapid and less uncertain goal inference, highlighting
the importance of verbal communication for cooperative agents.
