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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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Abstract
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.
Date issued
2009-12
URI
http://hdl.handle.net/1721.1/61347
Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology. School of Humanities, Arts, and Social Sciences
Journal
Advances in Neural Information Processing Systems 22
Publisher
Neural Information Processing Systems Foundation
Citation
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.
Version: Author's final manuscript
ISBN
9781615679119

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