2008-07-09-hajishirzi08a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
Sampling first order logical particles	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	hajishirzi08a	0	Sampling first order logical particles	248	255	248-255	248	false	McAllester, David A. and Myllym{"a}ki, Petri	given family David A. McAllester given family Petri Myllymäki	Hajishirzi, Hannaneh and Amir, Eyal	given family Hannaneh Hajishirzi given family Eyal Amir	2008-07-09	Reissued by PMLR on 30 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	http://proceedings.mlr.press/r6/hajishirzi08a/hajishirzi08a.pdf