2012/06/13 by Hannaneh Hajishirzi, Hajishirzi, Hannaneh, Eyal Amir +1 · 2 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning and Algorithms #cs.AI
paper · pdf · doi:10.48550/arxiv.1206.3264
Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008)
arxiv created 2012/06/13 · openalex publication_date 2012/06/13 · arxiv updated 2012/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Approximate inference in dynamic systems is the problem of estimating the state of the system given a sequence of actions and partial observations. High precision estimation is fundamental in many applications like diagnosis, natural language processing, tracking, planning, and robotics. In this paper we present an algorithm that samples possible deterministic executions of a probabilistic sequence. The algorithm takes advantage of a compact representation (using first order logic) for actions and world states to improve the precision of its estimation. Theoretical and empirical results show that the algorithm's expected error is smaller than propositional sampling and Sequential Monte Carlo (SMC) sampling techniques.