Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation

We study distributed average consensus problems in multi-agent systems with directed communication links that are subject to quantized information flow. The goal of distributed average consensus is for the nodes, each associated with some initial value, to obtain the average (or some value close to the average) of these initial values. In this paper, we present and analyze a distributed averaging algorithm which operates exclusively with quantized values (specifically, the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We characterize the properties of the proposed distributed averaging protocol on quantized values and show that its execution, on any time-invariant and strongly connected digraph, will allow all agents to reach, in finite time, a common consensus value represented as the ratio of two integer that is equal to the exact average. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm

Solving specified-time distributed optimization problem via sampled-data-based algorithm

Despite significant advances on distributed continuous-time optimization of multi-agent networks, there is still lack of an efficient algorithm to achieve the goal of distributed optimization at a pre-specified time. Herein, we design a specified-time distributed optimization algorithm for connected agents with directed topologies to collectively minimize the sum of individual objective functions subject to an equality constraint. With the designed algorithm, the settling time of distributed optimization can be exactly predefined. The specified selection of such a settling time is independent of not only the initial conditions of agents, but also the algorithm parameters and the communication topologies. Furthermore, the proposed algorithm can realize specified-time optimization by exchanging information among neighbours only at discrete sampling instants and thus reduces the communication burden. In addition, the equality constraint is always satisfied during the whole process, which makes the proposed algorithm applicable to online solving distributed optimization problems such as economic dispatch. For the special case of undirected communication topologies, a reduced-order algorithm is also designed. Finally, the effectiveness of the theoretical analysis is justified by numerical simulations

Interacting mindreaders

Could interacting mindreaders be in a position to know things which they would be unable to know if they were manifestly passive observers? This paper argues that they could. Mindreading is sometimes reciprocal: the mindreader's target reciprocates by taking the mindreader as a target for mindreading. The paper explains how such reciprocity can significantly narrow the range of possible interpretations of behaviour where mindreaders are, or appear to be, in a position to interact. A consequence is that revisions and extensions are needed to standard theories of the evidential basis of mindreading. The view also has consequences for understanding how abilities to interact combined with comparatively simple forms of mindreading may explain the emergence, in evolution or development, of sophisticated forms of social cognition