Softstar: Heuristic-guided probabilistic inference
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5889-softstar-heuristic-guided-probabilistic-inference.pdf
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Author(s)
Monfort, Mathew
Lake, Brenden M.
Ziebart, Brian
Lucey, Patrick
Tenenbaum, Joshua B
Date Issued
2015
Journal
Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS 2015)
Publisher
Neural Information Processing Systems Foundation, Inc.
Citation
Monfort, Mathew et al. "Softstar: Heuristic-guided probabilistic inference." Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS 2015), December 7-12 2015, Montreal, Canada, Neural Information Processing Systems Foundation, 2015 © 2015 Neural Information Processing Systems Foundation Inc
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/5889-softstar-heuristic-guided-probabilistic-inference
