Softstar: Heuristic-Guided Probabilistic Inference
Mathew Monfort, Brenden M Lake, Brian Ziebart, Patrick Lucey, Josh Tenenbaum
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.
BibTeX
@inproceedings{NIPS2015_ed422773,
author = {Monfort, Mathew and Lake, Brenden M and Ziebart, Brian and Lucey, Patrick and Tenenbaum, Josh},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Softstar: Heuristic-Guided Probabilistic Inference},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/ed4227734ed75d343320b6a5fd16ce57-Paper.pdf},
volume = {28},
year = {2015}
}