Help or hinder: Bayesian models of social goal inference
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...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Neural Information Processing Systems Foundation
2011
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| Online Access: | http://hdl.handle.net/1721.1/61347 https://orcid.org/0000-0002-1925-2035 https://orcid.org/0000-0002-9773-7871 https://orcid.org/0000-0001-7870-4487 https://orcid.org/0000-0003-1722-2382 |
| _version_ | 1811075464505065472 |
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| author | Ullman, Tomer David Tenenbaum, Joshua B. Baker, Christopher Lawrence Macindoe, Owen Evans, Owain Rhys Goodman, Noah D. |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Ullman, Tomer David Tenenbaum, Joshua B. Baker, Christopher Lawrence Macindoe, Owen Evans, Owain Rhys Goodman, Noah D. |
| author_sort | Ullman, Tomer David |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:06:26Z |
| format | Article |
| id | mit-1721.1/61347 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:06:26Z |
| publishDate | 2011 |
| publisher | Neural Information Processing Systems Foundation |
| record_format | dspace |
| spelling | mit-1721.1/613472022-09-26T15:46:10Z Help or hinder: Bayesian models of social goal inference Ullman, Tomer David Tenenbaum, Joshua B. Baker, Christopher Lawrence Macindoe, Owen Evans, Owain Rhys Goodman, Noah D. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Massachusetts Institute of Technology. School of Humanities, Arts, and Social Sciences Tenenbaum, Joshua B. Ullman, Tomer David Tenenbaum, Joshua B. Baker, Christopher Lawrence Macindoe, Owen Evans, Owain Rhys Goodman, Noah D. 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. United States. Army Research Office (ARO MURI grant W911NF-08-1-0242) United States. Air Force Office of Scientific Research (MURI grant FA9550-07-1-0075) National Science Foundation (U.S.) (Graduate Research Fellowship) James S. McDonnell Foundation (Collaborative Interdisciplinary Grant on Causal Reasoning) 2011-02-25T20:57:28Z 2011-02-25T20:57:28Z 2009-12 Article http://purl.org/eprint/type/ConferencePaper 9781615679119 http://hdl.handle.net/1721.1/61347 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. https://orcid.org/0000-0002-1925-2035 https://orcid.org/0000-0002-9773-7871 https://orcid.org/0000-0001-7870-4487 https://orcid.org/0000-0003-1722-2382 en_US http://books.nips.cc/papers/files/nips22/NIPS2009_1192.pdf Advances in Neural Information Processing Systems 22 Attribution-Noncommercial-Share Alike 3.0 Unported http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Neural Information Processing Systems Foundation MIT web domain |
| spellingShingle | Ullman, Tomer David Tenenbaum, Joshua B. Baker, Christopher Lawrence Macindoe, Owen Evans, Owain Rhys Goodman, Noah D. Help or hinder: Bayesian models of social goal inference |
| title | Help or hinder: Bayesian models of social goal inference |
| title_full | Help or hinder: Bayesian models of social goal inference |
| title_fullStr | Help or hinder: Bayesian models of social goal inference |
| title_full_unstemmed | Help or hinder: Bayesian models of social goal inference |
| title_short | Help or hinder: Bayesian models of social goal inference |
| title_sort | help or hinder bayesian models of social goal inference |
| url | http://hdl.handle.net/1721.1/61347 https://orcid.org/0000-0002-1925-2035 https://orcid.org/0000-0002-9773-7871 https://orcid.org/0000-0001-7870-4487 https://orcid.org/0000-0003-1722-2382 |
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