Help or Hinder: Bayesian Models of Social Goal Inference

Part of Advances in Neural Information Processing Systems 22 (NIPS 2009)

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Tomer Ullman, Chris Baker, Owen Macindoe, Owain Evans, Noah Goodman, Joshua Tenenbaum


Everyday social interactions are heavily influenced by our snap judgments about others goals. Even young infants can infer the goals of intentional agents from observing how they interact with objects and other agents in their environment: e.g., that one agent is helping orhindering anothers attempt to get up a hill or open a box. We propose a model for how people can infer these social goals from actions, based on inverse planning in multiagent Markov decision problems (MDPs). The model infers the goal most likely to be driving an agents behavior by assuming the agent acts approximately rationally given environmental constraints and its model of other agents present. We also present behavioral evidence in support of this model over a simpler, perceptual cue-based alternative.