Event-Triggered Control (ETC)
Recent developments in computer and communication technologies have led to a new type of large-scale resource-constrained wireless embedded control systems. It is desirable in these systems to limit the sensor and control computation and/or communication to instances when the system needs attention. However, classical sampled-data control is based on performing sensing and actuation periodically rather than when the system needs attention. This paper provides an introduction to event- and self-triggered control systems where sensing and actuation is performed when needed. Event-triggered control is reactive and generates sensor sampling and control actuation when, for instance, the plant state deviates more than a certain threshold from a desired value. Self-triggered control, on the other hand, is proactive and computes the next sampling or actuation instance ahead of time. The basics of these control strategies are introduced together with a discussion on the differences between state feedback and output feedback for event-triggered control. It is also shown how event- and self-triggered control can be implemented using existing wireless communication technology. Some applications to wireless control in process industry are discussed as well.
Deep Reinforcement Learning for Event-Triggered Control
Event-Triggered and Self-Triggered Control …

Decentralized Exploration Problem
We consider the \textit {decentralized exploration problem}: a set of players collaborate to identify the best arm by asynchronously interacting with the same stochastic environment. The objective is to insure privacy in the best arm identification problem between asynchronous, collaborative, and thrifty players. In the context of a digital service, we advocate that this decentralized approach allows a good balance between the interests of users and those of service providers: the providers optimize their services, while protecting the privacy of the users and saving resources. We define the privacy level as the amount of information an adversary could infer by intercepting the messages concerning a single user. We provide a generic algorithm {\sc Decentralized Elimination}, which uses any best arm identification algorithm as a subroutine. We prove that this algorithm insures privacy, with a low communication cost, and that in comparison to the lower bound of the best arm identification problem, its sample complexity suffers from a penalty depending on the inverse of the probability of the most frequent players. Finally, we propose an extension of the proposed algorithm to the non-stationary bandits. Experiments illustrate and complete the analysis. …

Ellsberg’s Paradox
The Ellsberg paradox is a paradox in decision theory in which people’s choices violate the postulates of subjective expected utility. It is generally taken to be evidence for ambiguity aversion. The paradox was popularized by Daniel Ellsberg, although a version of it was noted considerably earlier by John Maynard Keynes. The basic idea is that people overwhelmingly prefer taking on risk in situations where they know specific odds rather than an alternative risk scenario in which the odds are completely ambiguous – they will always choose a known probability of winning over an unknown probability of winning even if the known probability is low and the unknown probability could be a guarantee of winning. For example, given a choice of risks to take (such as bets), people ‘prefer the devil they know’ rather than assuming a risk where odds are difficult or impossible to calculate. Ellsberg proposed two separate thought experiments, the proposed choices in which contradict subjective expected utility. The 2-color problem involves bets on two urns, both of which contain balls of two different colors. The 3-color problem, described below, involves bets on a single urn, which contains balls of three different colors. …

Statistical Archetypal Analysis (SAA)
Statistical Archetypal Analysis (SAA) is introduced for the dimensional reduction of a collection of probability distributions known via samples. Applications include medical diagnosis from clinical data in the form of distributions (such as distributions of blood pressure or heart rates from different patients), the analysis of climate data such as temperature or wind speed at different locations, and the study of bifurcations in stochastic dynamical systems. Distributions can be embedded into a Hilbert space with a suitable metric, and then analyzed similarly to feature vectors in Euclidean space. However, most dimensional reduction techniques –such as Principal Component Analysis– are not interpretable for distributions, as neither the components nor the reconstruction of input data by components are themselves distributions. To obtain an interpretable result, Archetypal Analysis (AA) is extended to distributions, requiring the components to be mixtures of the input distributions and approximating the input distributions by mixtures of components. …