Online Bayesian Goal Inference for Boundedly-Rational Planning Agents

Understand

People routinely infer the goals of others by observing their actions over time.

• Remarkably, we can do so even when those actions lead to failure, enabling us to assist others when we detect that they might not achieve their goals.
• How might we endow machines with similar capabilities? Here we present an architecture capable of inferring an agent's goals online from both optimal and non-optimal sequences of actions.
• Our architecture models agents as boundedly-rational planners that interleave search with execution by replanning, thereby accounting for sub-optimal behavior.