Uncertainty in Artificial Intelligence
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Sampling First Order Logical Particles
Hannaneh Hajishirzi, Eyal Amir
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.
Pages: 248-255
PS Link:
PDF Link: /papers/08/p248-hajishirzi.pdf
AUTHOR = "Hannaneh Hajishirzi and Eyal Amir",
TITLE = "Sampling First Order Logical Particles",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "248--255"

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