scholarly journals Discovering the Influences of Complex Network Effects on Recovering Large Scale Multiagent Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yang Xu ◽  
Pengfei Liu ◽  
Xiang Li ◽  
Wei Ren
Keyword(s):  
Complex Network ◽  
Multiagent Systems ◽  
Network Topology ◽  
System Performance ◽  
Large Scale ◽  
Network Effects ◽  
Multiagent System ◽  
Distributed Coordination ◽  
Key Factor ◽  
Network Topologies

Building efficient distributed coordination algorithms is critical for the large scale multiagent system design, and the communication network has been shown as a key factor to influence system performance even under the same coordination protocol. Although many distributed algorithm designs have been proved to be feasible to build their functions in the large scale multiagent systems as claimed, the performances may not be stable if the multiagent networks were organized with different complex network topologies. For example, if the network was recovered from the broken links or disfunction nodes, the network topology might have been shifted. Therefore, their influences on the overall multiagent system performance are unknown. In this paper, we have made an initial effort to find how a standard network recovery policy, MPLS algorithm, may change the network topology of the multiagent system in terms of network congestion. We have established that when the multiagent system is organized as different network topologies according to different complex network attributes, the network shifts in different ways. Those interesting discoveries are helpful to predict how complex network attributes influence on system performance and in turn are useful for new algorithm designs that make a good use of those attributes.

scholarly journals Reorganizing Complex Network to Improve Large-Scale Multiagent Teamwork

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Yang Xu ◽  
Pengfei Liu ◽  
Xiang Li
Keyword(s):  
Complex Network ◽  
Team Performance ◽  
Large Scale ◽  
Network Effects ◽  
Human Society ◽  
Key Factors ◽  
Network Adjustment ◽  
Integrated Network ◽  
Intrinsic Nature ◽  
Key Factor

Large-scale multiagent teamwork has been popular in various domains. Similar to human society infrastructure, agents only coordinate with some of the others, with a peer-to-peer complex network structure. Their organization has been proven as a key factor to influence their performance. To expedite team performance, we have analyzed that there are three key factors. First, complex network effects may be able to promote team performance. Second, coordination interactions coming from their sources are always trying to be routed to capable agents. Although they could be transferred across the network via different paths, their sources and sinks depend on the intrinsic nature of the team which is irrelevant to the network connections. In addition, the agents involved in the same plan often form a subteam and communicate with each other more frequently. Therefore, if the interactions between agents can be statistically recorded, we are able to set up an integrated network adjustment algorithm by combining the three key factors. Based on our abstracted teamwork simulations and the coordination statistics, we implemented the adaptive reorganization algorithm. The experimental results briefly support our design that the reorganized network is more capable of coordinating heterogeneous agents.

scholarly journals Consensus Analysis of High-Order Multiagent Systems with General Topology and Asymmetric Time-Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Fangcui Jiang
Keyword(s):  
Multiagent Systems ◽  
Network Topology ◽  
Time Delays ◽  
Multiagent System ◽  
High Order ◽  
Directed Network ◽  
Time Varying ◽  
Consensus Analysis ◽  
Robust Consensus ◽  
Selection Of

This paper focuses on the consensus problem for high-order multiagent systems (MAS) with directed network and asymmetric time-varying time-delays. It is proved that the high-order multiagent system can reach consensus when the network topology contains a spanning tree and time-delay is bounded. The main contribution of this paper is that a Lyapunov-like design framework for the explicit selection of protocol parameters is provided. The Lyapunov-like design guarantees the robust consensus of the high-order multiagent system with respect to asymmetric time-delays and is independent of the exact knowledge of the topology when the communication linkages among agents are undirected and connected.

scholarly journals Modeling and Simulation of Complex Network Attributes on Coordinating Large Multiagent System

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Yang Xu ◽  
Xiang Li ◽  
Ming Liu
Keyword(s):  
Complex Network ◽  
Multiagent Systems ◽  
Decision Problem ◽  
Multiagent System ◽  
Algorithm Design ◽  
Distributed Agents ◽  
Coordination Strategies ◽  
Coordination Strategy ◽  
Significant Difference ◽  
Present Simulation

With the expansion of distributed multiagent systems, traditional coordination strategy becomes a severe bottleneck when the system scales up to hundreds of agents. The key challenge is that in typical large multiagent systems, sparsely distributed agents can only communicate directly with very few others and the network is typically modeled as an adaptive complex network. In this paper, we present simulation testbedCoordSimbuilt to model the coordination of network centric multiagent systems. Based on the token-based strategy, the coordination can be built as a communication decision problem that agents make decisions to target communications and pass them over to the capable agents who will potentially benefit the team most. We have theoretically analyzed that the characters of complex network make a significant difference with both random and intelligent coordination strategies, which may contribute to future multiagent algorithm design.

scholarly journals Emergence of network effects and predictability in the judicial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Enys Mones ◽  
Piotr Sapieżyński ◽  
Simon Thordal ◽  
Henrik Palmer Olsen ◽  
Sune Lehmann
Keyword(s):  
Complex Network ◽  
Large Scale ◽  
Network Effects ◽  
Citation Network ◽  
Judicial System ◽  
Science Methods ◽  
The European Union ◽  
Legal Challenges ◽  
Consistent Increase ◽  
Over Time

AbstractAs courts strive to simultaneously remain self-consistent and adapt to new legal challenges, a complex network of of citations between decided cases is established. Using network science methods to analyze the underlying patterns of citations between cases can help us understand the large-scale mechanisms which shape the judicial system. Here, we use the case-to-case citation structure of the Court of Justice of the European Union to examine this question. Using a link-prediction model, we show that over time the complex network of citations evolves in a way which improves our ability to predict new citations. Investigating the factors which enable prediction over time, we find that the content of the case documents plays a decreasing role, whereas both the predictive power and significance of the citation network structure itself show a consistent increase over time. Finally, our analysis enables us to validate existing citations and recommend potential citations for future cases within the court.

scholarly journals Performance Analysis of Edge Computing in IoT

2020 ◽  
Vol 26 (1) ◽  
pp. 72-77
Author(s):  
Julius Skirelis ◽  
Dalius Navakauskas
Keyword(s):  
Performance Analysis ◽  
Network Topology ◽  
System Performance ◽  
State Of The Art ◽  
Edge Computing ◽  
Network Bandwidth ◽  
Network Parameters ◽  
Network Topologies ◽  
Considerable Impact ◽  
Art Research

Upcoming 5G technology and the demand for near real-time response services raise the need for optimizing current IoT solutions. The aim of this paper is to model and execute the performance analysis of network structures suitable for Edge Computing in IoT. The prior research into different topology and parameter sets have not provided sufficient clarity, on which parameters had a considerable impact on overall system performance; therefore, repetitive simulations were performed to express dispersion of alternating values, as well as determining its confidence intervals. The paper presents Edge Computing service simulation setup on known and newly derived network topologies with different complexity varying network bandwidth and network delay parameters. The experimental investigation has revealed that the IoT configuration network is more sensitive to network topology, while the Internet configuration is more sensitive to network parameters. The discussion on the results received debates possible causes of performance differences in network parameters and device configurations, the comparison to similar state-of-the-art research results has also been presented. Finally, conclusions with recommendations based on the results acquired have been provided.

scholarly journals Effects of network topology on the performance of consensus and distributed learning of SVMs using ADMM

2021 ◽  
Vol 7 ◽  
pp. e397
Author(s):  
Shirin Tavara ◽  
Alexander Schliep
Keyword(s):  
Network Topology ◽  
Large Scale ◽  
Learning Problems ◽  
Support Vector ◽  
Spectral Gaps ◽  
Distributed Framework ◽  
Alternating Direction ◽  
Communication Time ◽  
Network Topologies ◽  
The Impact

The Alternating Direction Method of Multipliers (ADMM) is a popular and promising distributed framework for solving large-scale machine learning problems. We consider decentralized consensus-based ADMM in which nodes may only communicate with one-hop neighbors. This may cause slow convergence. We investigate the impact of network topology on the performance of an ADMM-based learning of Support Vector Machine using expander, and mean-degree graphs, and additionally some of the common modern network topologies. In particular, we investigate to which degree the expansion property of the network influences the convergence in terms of iterations, training and communication time. We furthermore suggest which topology is preferable. Additionally, we provide an implementation that makes these theoretical advances easily available. The results show that the performance of decentralized ADMM-based learning of SVMs in terms of convergence is improved using graphs with large spectral gaps, higher and homogeneous degrees.

scholarly journals An Optimal Rewiring Strategy for Cooperative Multiagent Social Learning

2019 ◽  
Vol 33 ◽  
pp. 10049-10050
Author(s):  
Hongyao Tang ◽  
Jianye Hao ◽  
Li Wang ◽  
Tim Baarslag ◽  
Zan Wang
Keyword(s):  
Social Learning ◽  
Multiagent Systems ◽  
Real World ◽  
Large Scale ◽  
Dynamic Environment ◽  
Multiagent Coordination ◽  
Learning Framework ◽  
Repeated Interaction ◽  
Long Run ◽  
Network Topologies

Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and static social learning framework. However, two aspects of dynamics in real-world MASs are currently missing. First, the network topologies can dynamically change during the course of interaction. Second, the interaction utilities between each pair of agents may not be identical and not known as a prior. Both issues mentioned above increase the difficulty of coordination. In this paper, we consider the multiagent social learning in a dynamic environment in which agents can alter their connections and interact with randomly chosen neighbors with unknown utilities beforehand. We propose an optimal rewiring strategy to select most beneficial peers to maximize the accumulated payoffs in long-run interactions. We empirically demonstrate the effects of our approach in large-scale MASs.

Going the Distance

2020 ◽  
Author(s):  
Ron Harris
Keyword(s):  
Large Scale ◽  
Western Europe ◽  
Family Firms ◽  
Global Trade ◽  
Long Distance ◽  
Business Corporation ◽  
Modern Economy ◽  
Key Factor ◽  
Silk Route ◽  
Passive Investors

Before the seventeenth century, trade across Eurasia was mostly conducted in short segments along the Silk Route and Indian Ocean. Business was organized in family firms, merchant networks, and state-owned enterprises, and dominated by Chinese, Indian, and Arabic traders. However, around 1600 the first two joint-stock corporations, the English and Dutch East India Companies, were established. This book tells the story of overland and maritime trade without Europeans, of European Cape Route trade without corporations, and of how new, large-scale, and impersonal organizations arose in Europe to control long-distance trade for more than three centuries. It shows that by 1700, the scene and methods for global trade had dramatically changed: Dutch and English merchants shepherded goods directly from China and India to northwestern Europe. To understand this transformation, the book compares the organizational forms used in four major regions: China, India, the Middle East, and Western Europe. The English and Dutch were the last to leap into Eurasian trade, and they innovated in order to compete. They raised capital from passive investors through impersonal stock markets and their joint-stock corporations deployed more capital, ships, and agents to deliver goods from their origins to consumers. The book explores the history behind a cornerstone of the modern economy, and how this organizational revolution contributed to the formation of global trade and the creation of the business corporation as a key factor in Europe's economic rise.

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