Softstar: Heuristic-guided probabilistic inference
Recent machine learning methods for sequential behavior prediction estimate the motives of behavior rather than the behavior itself. This higher-level abstraction improves generalization in different prediction settings, but computing predictions often becomes intractable in large decision spaces. W...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Published: |
Neural Information Processing Systems Foundation, Inc.
2017
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| Online Access: | http://hdl.handle.net/1721.1/112751 https://orcid.org/0000-0002-1925-2035 |
| Summary: | Recent machine learning methods for sequential behavior prediction estimate the motives of behavior rather than the behavior itself. This higher-level abstraction improves generalization in different prediction settings, but computing predictions often becomes intractable in large decision spaces. We propose the Softstar algorithm, a softened heuristic-guided search technique for the maximum entropy inverse optimal control model of sequential behavior. This approach supports probabilistic search with bounded approximation error at a significantly reduced computational cost when compared to sampling based methods. We present the algorithm, analyze approximation guarantees, and compare performance with simulation-based inference on two distinct complex decision tasks. |
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