Sampling First Order Logical Particles
Hannaneh Hajishirzi
and
Eyal Amir
Abstract:
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.
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