  We study a pull-based communication system where a sensing agent updates an
actuation agent using a query control policy, which is adjusted in the
evolution of an observed information source and the usefulness of each update
for achieving a specific goal. For that, a controller decides whether to pull
an update at each slot, predicting what is probably occurring at the source and
how much effective impact that update could have at the endpoint. Thus,
temporal changes in the source evolution could modify the query arrivals so as
to capture important updates. The amount of impact is determined by a grade of
effectiveness (GoE) metric, which incorporates both freshness and usefulness
attributes of the communicated updates. Applying an iterative algorithm, we
derive query decisions that maximize the long-term average GoE for the
communicated packets, subject to cost constraints. Our analytical and numerical
results show that the proposed query policy exhibits higher effectiveness than
existing periodic and probabilistic query policies for a wide range of query
arrival rates.
