Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model
Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of their actions is typically unobservable, and must be inferred in order to learn new skills by demonstration, or to assist...
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| Format: | Article |
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Association for the Advancement of Artificial Intelligence
2017
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| Online Access: | http://hdl.handle.net/1721.1/112656 https://orcid.org/0000-0001-7870-4487 https://orcid.org/0000-0002-1925-2035 |
| _version_ | 1826199728824516608 |
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| author | Nakahashi, Ryo Baker, Christopher Lawrence Tenenbaum, Joshua B |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Nakahashi, Ryo Baker, Christopher Lawrence Tenenbaum, Joshua B |
| author_sort | Nakahashi, Ryo |
| collection | MIT |
| description | Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of their actions is typically unobservable, and must be inferred in order to learn new skills by demonstration, or to assist others in completing their tasks. For example, an assistant who has learned the subgoal structure of a colleague's task can more rapidly recognize and support their actions as they unfold. Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences. We test this model with a behavioral experiment in which humans observed different series of goal-directed actions, and inferred both the number and composition of the subgoal sequences associated with each goal. The Bayesian model predicts human subgoal inferences with high accuracy, and significantly better than several alternative models and straightforward heuristics. Motivated by this result, we simulate how learning and inference of subgoals can improve performance in an artificial user assistance task. The Bayesian model learns the correct subgoals from fewer observations, and better assists users by more rapidly and accurately inferring the goal of their actions than alternative approaches. |
| first_indexed | 2024-09-23T11:25:08Z |
| format | Article |
| id | mit-1721.1/112656 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:25:08Z |
| publishDate | 2017 |
| publisher | Association for the Advancement of Artificial Intelligence |
| record_format | dspace |
| spelling | mit-1721.1/1126562022-10-01T03:28:08Z Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model Nakahashi, Ryo Baker, Christopher Lawrence Tenenbaum, Joshua B Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Nakahashi, Ryo Baker, Christopher Lawrence Tenenbaum, Joshua B Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of their actions is typically unobservable, and must be inferred in order to learn new skills by demonstration, or to assist others in completing their tasks. For example, an assistant who has learned the subgoal structure of a colleague's task can more rapidly recognize and support their actions as they unfold. Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences. We test this model with a behavioral experiment in which humans observed different series of goal-directed actions, and inferred both the number and composition of the subgoal sequences associated with each goal. The Bayesian model predicts human subgoal inferences with high accuracy, and significantly better than several alternative models and straightforward heuristics. Motivated by this result, we simulate how learning and inference of subgoals can improve performance in an artificial user assistance task. The Bayesian model learns the correct subgoals from fewer observations, and better assists users by more rapidly and accurately inferring the goal of their actions than alternative approaches. 2017-12-08T16:12:24Z 2017-12-08T16:12:24Z 2016-02 2017-12-08T13:30:39Z Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/112656 Nakahashi, Ryo et al. "Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model." Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, Association for the Advancement of Artificial Intelligence, February 2016 © 2016 Association for the Advancement of Artificial Intelligence https://orcid.org/0000-0001-7870-4487 https://orcid.org/0000-0002-1925-2035 https://dl.acm.org/citation.cfm?id=3016432 Thirtieth AAAI Conference on Artificial Intelligence Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for the Advancement of Artificial Intelligence arXiv |
| spellingShingle | Nakahashi, Ryo Baker, Christopher Lawrence Tenenbaum, Joshua B Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model |
| title | Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model |
| title_full | Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model |
| title_fullStr | Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model |
| title_full_unstemmed | Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model |
| title_short | Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model |
| title_sort | modeling human understanding of complex intentional action with a bayesian nonparametric subgoal model |
| url | http://hdl.handle.net/1721.1/112656 https://orcid.org/0000-0001-7870-4487 https://orcid.org/0000-0002-1925-2035 |
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