Computational Models of Goal Inference in Open-Ended Domains

Abstract

How are people able to quickly infer the goals of others when observing just a few of their actions? This work investigates the cognitive mechanisms underlying human goal inference by building and evaluating computational models that capture how people make these goal inferences in unstructured domains. Drawing on the frame- work of Bayesian inference, I formalize the idea that people interpret others’ actions by determining how consistent their observed evidence is with given goal hypotheses. I built a cooking domain called Recipe-Graph and ran human experiments to under- stand how well human predictions agree with those of our computational models. I find that people’s goal predictions correlate with those of our models, suggesting that Bayesian inference can capture how people predict the goals of others around them.