1,560 research outputs found
Pooling or sampling: Collective dynamics for electrical flow estimation
The computation of electrical flows is a crucial primitive for many recently proposed optimization algorithms on weighted networks. While typically implemented as a centralized subroutine, the ability to perform this task in a fully decentralized way is implicit in a number of biological systems. Thus, a natural question is whether this task can provably be accomplished in an efficient way by a network of agents executing a simple protocol. We provide a positive answer, proposing two distributed approaches to electrical flow computation on a weighted network: a deterministic process mimicking Jacobi's iterative method for solving linear systems, and a randomized token diffusion process, based on revisiting a classical random walk process on a graph with an absorbing node. We show that both processes converge to a solution of Kirchhoff's node potential equations, derive bounds on their convergence rates in terms of the weights of the network, and analyze their time and message complexity
Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning
We propose a fully distributed actor-critic algorithm approximated by deep
neural networks, named \textit{Diff-DAC}, with application to single-task and
to average multitask reinforcement learning (MRL). Each agent has access to
data from its local task only, but it aims to learn a policy that performs well
on average for the whole set of tasks. During the learning process, agents
communicate their value-policy parameters to their neighbors, diffusing the
information across the network, so that they converge to a common policy, with
no need for a central node. The method is scalable, since the computational and
communication costs per agent grow with its number of neighbors. We derive
Diff-DAC's from duality theory and provide novel insights into the standard
actor-critic framework, showing that it is actually an instance of the dual
ascent method that approximates the solution of a linear program. Experiments
suggest that Diff-DAC can outperform the single previous distributed MRL
approach (i.e., Dist-MTLPS) and even the centralized architecture
Cooperative learning in multi-agent systems from intermittent measurements
Motivated by the problem of tracking a direction in a decentralized way, we
consider the general problem of cooperative learning in multi-agent systems
with time-varying connectivity and intermittent measurements. We propose a
distributed learning protocol capable of learning an unknown vector from
noisy measurements made independently by autonomous nodes. Our protocol is
completely distributed and able to cope with the time-varying, unpredictable,
and noisy nature of inter-agent communication, and intermittent noisy
measurements of . Our main result bounds the learning speed of our
protocol in terms of the size and combinatorial features of the (time-varying)
networks connecting the nodes
Autonomic Management for Multi-agent Systems
Autonomic computing is a computing system that can manage itself by
self-configuration, self-healing, self-optimizing and self-protection.
Researchers have been emphasizing the strong role that multi agent systems can
play progressively towards the design and implementation of complex autonomic
systems. The important of autonomic computing is to create computing systems
capable of managing themselves to a far greater extent than they do today. With
the nature of autonomy, reactivity, sociality and pro-activity, software agents
are promising to make autonomic computing system a reality. This paper mixed
multi-agent system with autonomic feature that completely hides its complexity
from users/services. Mentioned Java Application Development Framework as
platform example of this environment, could applied to web services as front
end to users. With multi agent support it also provides adaptability,
intelligence, collaboration, goal oriented interactions, flexibility, mobility
and persistence in software systemsComment: 4page,2figur
Distributed Policy Evaluation Under Multiple Behavior Strategies
We apply diffusion strategies to develop a fully-distributed cooperative
reinforcement learning algorithm in which agents in a network communicate only
with their immediate neighbors to improve predictions about their environment.
The algorithm can also be applied to off-policy learning, meaning that the
agents can predict the response to a behavior different from the actual
policies they are following. The proposed distributed strategy is efficient,
with linear complexity in both computation time and memory footprint. We
provide a mean-square-error performance analysis and establish convergence
under constant step-size updates, which endow the network with continuous
learning capabilities. The results show a clear gain from cooperation: when the
individual agents can estimate the solution, cooperation increases stability
and reduces bias and variance of the prediction error; but, more importantly,
the network is able to approach the optimal solution even when none of the
individual agents can (e.g., when the individual behavior policies restrict
each agent to sample a small portion of the state space).Comment: 36 pages, 4 figures, accepted for publication on IEEE Transactions on
Automatic Contro
Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty - An Extended Version
This paper presents HEALER, a software agent that recommends sequential
intervention plans for use by homeless shelters, who organize these
interventions to raise awareness about HIV among homeless youth. HEALER's
sequential plans (built using knowledge of social networks of homeless youth)
choose intervention participants strategically to maximize influence spread,
while reasoning about uncertainties in the network. While previous work
presents influence maximizing techniques to choose intervention participants,
they do not address three real-world issues: (i) they completely fail to scale
up to real-world sizes; (ii) they do not handle deviations in execution of
intervention plans; (iii) constructing real-world social networks is an
expensive process. HEALER handles these issues via four major contributions:
(i) HEALER casts this influence maximization problem as a POMDP and solves it
using a novel planner which scales up to previously unsolvable real-world
sizes; (ii) HEALER allows shelter officials to modify its recommendations, and
updates its future plans in a deviation-tolerant manner; (iii) HEALER
constructs social networks of homeless youth at low cost, using a Facebook
application. Finally, (iv) we show hardness results for the problem that HEALER
solves. HEALER will be deployed in the real world in early Spring 2016 and is
currently undergoing testing at a homeless shelter.Comment: This is an extended version of our AAMAS 2016 paper (with the same
name) with full proofs of all our theorems include
Cyber-Physical Modeling and Control of Crowd of Pedestrians: A Review and New Framework
Recent advances in modeling and control of crowds of pedestrians are briefly
surveyed in this paper. Possibilities of applying fractional calculus in the
modeling of crowd of pedestrians have been shortly reviewed and discussed from
different aspects such as descriptions of motion, interactions of long range
and effects of memory. Control of the crowd of pedestrians have also been
formulated using the framework of Cyber-Physical Systems and been realized
using networked Segways with onboard emergency response personnels to regulate
the velocity and flux of the crowd. Platform for verification of the
theoretical results are also provided in this paper.Comment: 16 pages, 3 figure
Lyapunov Approach to Consensus Problems
This paper investigates the weighted-averaging dynamic for unconstrained and
constrained consensus problems. Through the use of a suitably defined adjoint
dynamic, quadratic Lyapunov comparison functions are constructed to analyze the
behavior of weighted-averaging dynamic. As a result, new convergence rate
results are obtained that capture the graph structure in a novel way. In
particular, the exponential convergence rate is established for unconstrained
consensus with the exponent of the order of . Also, the
exponential convergence rate is established for constrained consensus, which
extends the existing results limited to the use of doubly stochastic weight
matrices
Online Distributed Optimization on Dynamic Networks
This paper presents a distributed optimization scheme over a network of
agents in the presence of cost uncertainties and over switching communication
topologies. Inspired by recent advances in distributed convex optimization, we
propose a distributed algorithm based on a dual sub-gradient averaging. The
objective of this algorithm is to minimize a cost function cooperatively.
Furthermore, the algorithm changes the weights on the communication links in
the network to adapt to varying reliability of neighboring agents. A
convergence rate analysis as a function of the underlying network topology is
then presented, followed by simulation results for representative classes of
sensor networks.Comment: Submitted to The IEEE Transactions on Automatic Control, 201
Wide-Area Time-Synchronized Closed-Loop Control of Power Systems And Decentralized Active Distribution Networks
The rapidly expanding power system grid infrastructure and the need to reduce the occurrence of major blackouts and prevention or hardening of systems against cyber-attacks, have led to increased interest in the improved resilience of the electrical grid. Distributed and decentralized control have been widely applied to computer science research. However, for power system applications, the real-time application of decentralized and distributed control algorithms introduce several challenges. In this dissertation, new algorithms and methods for decentralized control, protection and energy management of Wide Area Monitoring, Protection and Control (WAMPAC) and the Active Distribution Network (ADN) are developed to improve the resiliency of the power system. To evaluate the findings of this dissertation, a laboratory-scale integrated Wide WAMPAC and ADN control platform was designed and implemented. The developed platform consists of phasor measurement units (PMU), intelligent electronic devices (IED) and programmable logic controllers (PLC). On top of the designed hardware control platform, a multi-agent cyber-physical interoperability viii framework was developed for real-time verification of the developed decentralized and distributed algorithms using local wireless and Internet-based cloud communication. A novel real-time multiagent system interoperability testbed was developed to enable utility independent private microgrids standardized interoperability framework and define behavioral models for expandability and plug-and-play operation. The state-of-theart power system multiagent framework is improved by providing specific attributes and a deliberative behavior modeling capability. The proposed multi-agent framework is validated in a laboratory based testbed involving developed intelligent electronic device prototypes and actual microgrid setups. Experimental results are demonstrated for both decentralized and distributed control approaches. A new adaptive real-time protection and remedial action scheme (RAS) method using agent-based distributed communication was developed for autonomous hybrid AC/DC microgrids to increase resiliency and continuous operability after fault conditions. Unlike the conventional consecutive time delay-based overcurrent protection schemes, the developed technique defines a selectivity mechanism considering the RAS of the microgrid after fault instant based on feeder characteristics and the location of the IEDs. The experimental results showed a significant improvement in terms of resiliency of microgrids through protection using agent-based distributed communication
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