The rise of reinforcement learning (RL) has guided a new paradigm: unraveling the
intervention strategies to control systems with unknown dynamics. Model-free RL provides …

We propose a set of optimization techniques for transforming a generic AI codebase so that
it can be successfully deployed to a restricted serverless environment, without compromising …

This study describes an approach for attractor selection (or multistability control) in nonlinear
dynamical systems with constrained actuation. Attractor selection is obtained using two …

Abstract Probabilistic Boolean Networks (PBNs) were introduced as a computational model
for the study of complex dynamical systems, such as Gene Regulatory Networks (GRNs) …

The ability to direct a probabilistic Boolean network (PBN) to the desired state is important to
applications such as targeted therapeutics in cancer biology. Reinforcement learning (RL) …

This article explores the state-flipped control mechanism for the finite horizon output tracking
of Boolean control networks (BCNs) subject to a time-varying reference output trajectory …

This paper investigates the output tracking problem of switched Boolean networks using self-
triggered control. Firstly, by virtue of the constructed auxiliary system, two necessary and …

Gene regulatory networks represent collections of regulators that interact with each other
and with other molecules to govern gene expression. Biological signalling networks model …

Abstract Probabilistic Boolean Networks have been proposed for estimating the behaviour of
dynamical systems as they combine rule-based modelling with uncertainty principles …