326,749 research outputs found
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
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