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@inproceedings{vazquez-rss21,
author = {Marcell Vazquez{-}Chanlatte and
Sebastian Junges and
Daniel J. Fremont and
Sanjit Seshia},
title = {Entropy-Guided Control Improvisation},
booktitle = {Robotics: Science and Systems XVII (RSS)},
year = {2021},
abstract = {High level declarative constraints provide a powerful
(and popular) way to define and construct control policies;
however, most synthesis algorithms do not support specifying
the degree of randomness (unpredictability) of the resulting
controller. In many contexts, e.g., patrolling, testing, behavior
prediction, and planning on idealized models, predictable or
biased controllers are undesirable. To address these concerns,
we introduce the Entropic Reactive Control Improvisation (ERCI)
framework and algorithm which supports synthesizing control
policies for stochastic games that are declaratively specified by (i)
a hard constraint specifying what must occur, (ii) a soft constraint
specifying what typically occurs, and (iii) a randomization
constraint specifying the unpredictability and variety of the
controller, as quantified using causal entropy. This framework,
which extends the state of the art by supporting arbitrary
combinations of adversarial and probabilistic uncertainty in the
environment. ERCI enables a flexible modeling formalism which
we argue, theoretically and empirically, remains tractable.},
}