Help or hinder: Bayesian models of social goal inference
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Tenenbaum_Help or.pdf
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Author(s)
Ullman, Tomer David
Tenenbaum, Joshua B.
Baker, Christopher Lawrence
Macindoe, Owen
Evans, Owain Rhys
Goodman, Noah D.
Date Issued
December 2009
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. School of Humanities, Arts, and Social Sciences
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Attribution-Noncommercial-Share Alike 3.0 Unported
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DOI of Published Version
http://books.nips.cc/papers/files/nips22/NIPS2009_1192.pdf
