Softstar: Heuristic-guided probabilistic inference

Mathew Monfort, Brenden M. Lake, Brian D. Ziebart, Patrick Lucey, Joshua B. Tenenbaum

Research output: Contribution to journalConference article

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.

Original languageEnglish (US)
Pages (from-to)2764-2772
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume2015-January
StatePublished - 2015
Event29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada
Duration: Dec 7 2015Dec 12 2015

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Monfort, M., Lake, B. M., Ziebart, B. D., Lucey, P., & Tenenbaum, J. B. (2015). Softstar: Heuristic-guided probabilistic inference. Advances in Neural Information Processing Systems, 2015-January, 2764-2772.