– Nowadays packet classification is a fundamental task for network devices such as edge routers, firewalls and intrusion detection systems. Determining which flow packets belong to is important for many applications, and it is necessary, for example, to provide differentiated services, to detect anomalous traffic and to sort attack patterns. Therefore… (More)
In this paper we present a model predictive control (MPC) approach to optimize vehicle scheduling and routing in an autonomous mobility-on-demand (AMoD) system. In AMoD systems, robotic, self-driving vehicles transport customers within an urban environment and are coordinated to optimize service throughout the entire network. Specifically, we first propose… (More)
We propose an approach to extend the standard framework of dynamic games to settings with multi-product firms. Our approach applies to industries with a large number of products offered by a small number of firms.
For more than a century, fungal pathogens and symbionts have been known to orient hyphal growth towards chemical stimuli from the host plant. However, the nature of the plant signals as well as the mechanisms underlying the chemotropic response have remained elusive. Here we show that directed growth of the soil-inhabiting plant pathogen Fusarium oxysporum… (More)
The past decade has witnessed a rapidly growing interest in decentralized algorithms for collective decision-making in cyber-physical networks. For a large variety of settings, control strategies are now known that either minimize time complexity (i.e., convergence time) or optimize communication complexity (i.e., number and size of exchanged messages).… (More)
—This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing… (More)
— This paper studies fundamental limitations of performance for distributed decision-making in robotic networks. The class of decision-making problems we consider encompasses a number of prototypical problems such as average-based consensus as well as distributed optimization, leader election, majority voting on a limited number of options, MAX, MIN, and… (More)