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Summary: Learning Partially Observable Action Schemas
Dafna Shahaf and Eyal Amir
Computer Science Department
University of Illinois, Urbana-Champaign
Urbana, IL 61801, USA
{dshahaf2,eyal}@uiuc.edu
Abstract
We present an algorithm that derives actions' effects and pre-
conditions in partially observable, relational domains. Our
algorithm has two unique features: an expressive relational
language, and an exact tractable computation. An action-
schema language that we present permits learning of precon-
ditions and effects that include implicit objects and unstated
relationships between objects. For example, we can learn that
replacing a blown fuse turns on all the lights whose switch is
set to on. The algorithm maintains and outputs a relational-
logical representation of all possible action-schema models
after a sequence of executed actions and partial observations.
Importantly, our algorithm takes polynomial time in the num-
ber of time steps and predicates. Time dependence on other
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