Help or hinder: Bayesian models of social goal inference
Author(s)Ullman, Tomer David; Tenenbaum, Joshua B.; Baker, Christopher Lawrence; Macindoe, Owen; Evans, Owain Rhys; Goodman, Noah D.; ... Show more Show less
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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’ or ‘hindering’ another’s 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 agent’s 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.
DepartmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology. School of Humanities, Arts, and Social Sciences
Advances in Neural Information Processing Systems 22
Neural Information Processing Systems Foundation
Ullman, Tomer D., et al. "Help or Hinder: Bayesian Models of Social Goal Inference." Advances in Neural Information Processing Systems 22, Annual Conference on Neural Information Processing Systems, NIPS 2009.
Author's final manuscript