Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-Parametric Behavior Learning for Multi-Agent Decision Making With
  Chance Constraints: A Data-Driven Predictive Control Framework   In many specific scenarios, accurate and effective system identification is a
commonly encountered challenge in the model predictive control (MPC)
formulation. As a consequence, the overall system performance could be
significantly degraded in outcome when the traditional MPC algorithm is adopted
under those circumstances when such accuracy is lacking. To cater to this
rather major shortcoming, this paper investigates a non-parametric behavior
learning method for multi-agent decision making, which underpins an alternate
data-driven predictive control framework. Utilizing an innovative methodology
with closed-loop input/output measurements of the unknown system, the behavior
of the system is learned based on the collected dataset, and thus the
constructed non-parametric predictive model can be used for the determination
of optimal control actions. This non-parametric predictive control framework
attains the noteworthy key advantage of alleviating the heavy computational
burden commonly encountered in the optimization procedures otherwise involved.
Such requisite optimization procedures are typical in existing methodologies
requiring open-loop input/output measurement data collection and parametric
system identification. Then with a conservative approximation of probabilistic
chance constraints for the MPC problem, a resulting deterministic optimization
problem is formulated and solved effectively. This intuitive data-driven
approach is also shown to preserve good robustness properties (even in the
inevitable existence of parametric uncertainties that naturally arise in the
typical system identification process). Finally, a multi-drone system is used
to demonstrate the practical appeal and highly effective outcome of this
promising development.

24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int