Softstar: Heuristic-guided probabilistic inference
Mathew Monfort, Brenden M. Lake, Brian D. Ziebart, Patrick J. Lucey, Joshua B. Tenenbaum · DSpace@MIT (Massachusetts Institute of Technology) · 2015
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.