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Reasoning about Deterministic Action Sequences with Probabilistic Priors Hannaneh Hajishirzi and Eyal Amir
 

Summary: Reasoning about Deterministic Action Sequences with Probabilistic Priors
Hannaneh Hajishirzi and Eyal Amir
Department of Computer Science
University of Illinois at Urbana-Champaign
{hajishir, eyal}@illinois.edu
Paper number: #118
Abstract
We present a novel algorithm and a new understanding of rea-
soning about a sequence of deterministic actions with a prob-
abilistic prior. When the initial state of a dynamic system is
unknown, a probability distribution can be still specified over
the initial states. Estimating the posterior distribution over
states (filtering) after some deterministic actions occurred is
a problem relevant to AI planning, natural language process-
ing (NLP) and robotics among others. Current approaches
to filtering deterministic actions are not tractable even if the
distribution over the initial system state is represented com-
pactly. The reason is that state variables become correlated
after a few steps. The main innovation in this paper is a
method for sidestepping this problem by redefining state vari-

  

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

 

Collections: Computer Technologies and Information Sciences