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Stochastic Filtering in a Probabilistic Action Model Hannaneh Hajishirzi and Eyal Amir
 

Summary: Stochastic Filtering in a Probabilistic Action Model
Hannaneh Hajishirzi and Eyal Amir
Computer Science Department
University of Illinois at Urbana-Champaign
Urbana, IL 61801. USA
{hajishir, eyal}uiuc.edu
Abstract
Stochastic filtering is the problem of estimating the state of
a dynamic system after time passes and given partial obser-
vations. It is fundamental to automatic tracking, planning,
and control of real-world stochastic systems such as robots,
programs, and autonomous agents. This paper presents a
novel sampling-based filtering algorithm. Its expected er-
ror is smaller than sequential Monte Carlo sampling tech-
niques given a fixed number of samples, as we prove and
show empirically. It does so by sampling deterministic ac-
tion sequences, and exact filtering of those sequences. These
results are promising for applications in stochastic planning,
natural language processing, and robot control.
1 Introduction

  

Source: Amir, Eyal - Department of Computer Science, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences