Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to act: a Reinforcement Learning approach to recommend the best
  next activities   The rise of process data availability has led in the last decade to the
development of several data-driven learning approaches. However, most of these
approaches limit themselves to use the learned model to predict the future of
ongoing process executions. The goal of this paper is moving a step forward and
leveraging data with the purpose of learning to act by supporting users with
recommendations for the best strategy to follow, in order to optimize a measure
of performance. In this paper, we take the (optimization) perspective of one
process actor and we recommend the best activities to execute next, in response
to what happens in a complex external environment, where there is no control on
exogenous factors. To this aim, we investigate an approach that learns, by
means of Reinforcement Learning, an optimal policy from the observation of past
executions and recommends the best activities to carry on for optimizing a Key
Performance Indicator of interest. The potentiality of the approach has been
demonstrated on two scenarios taken from real-life data.

34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph