Planning to Chronicle
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by
Hazhar Rahmani, Dylan A. Shell, Jason M. O'Kane
2020
Abstract
An important class of applications entails a robot monitoring, scrutinizing,
or recording the evolution of an uncertain time-extended process. This sort of
situation leads an interesting family of planning problems in which the robot
is limited in what it sees and must, thus, choose what to pay attention to. The
distinguishing characteristic of this setting is that the robot has influence
over what it captures via its sensors, but exercises no causal authority over
the evolving process. As such, the robot's objective is to observe the
underlying process and to produce a `chronicle' of occurrent events, subject to
a goal specification of the sorts of event sequences that may be of interest.
This paper examines variants of such problems when the robot aims to collect
sets of observations to meet a rich specification of their sequential
structure. We study this class of problems by modeling a stochastic process via
a variant of a hidden Markov model, and specify the event sequences of interest
as a regular language, developing a vocabulary of `mutators' that enable
sophisticated requirements to be expressed. Under different suppositions about
the information gleaned about the Markov model, we formulate and solve
different planning problems. The core underlying idea is the construction of a
product between the event model and a specification automaton. The paper
reports and compares performance metrics by drawing on some small case studies
analyzed in depth in simulation.
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2011.02135v1
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