scholarly journals Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

2022 ◽  
pp. 129-140
Author(s):  
Mattia Brambilla
Keyword(s):  
Situational Awareness ◽  
Dynamic Environment ◽  
General Context ◽  
Cooperative Localization ◽  
Multi Agent Systems ◽  
Communication Techniques ◽  
Research Areas ◽  
Multi Agent ◽  
Types Of Information ◽  
Agent Networks

AbstractThis brief highlights research advances on cooperative techniques for localization and communication. These two macro trends are investigated in the general context of mobile multi-agent networks for situational awareness applications, where time-varying agents of unknown locations are asked to fulfill positioning and information sharing tasks. Cooperative localization is conceived for both active and passive agents, i.e., targets to be detected and localized, and it is analyzed in vehicular and maritime environments. Communication is investigated for vehicular scenarios, where vehicles are requested to share massive data in the perspective development of connected and automated mobility systems. Both research areas rely on the integration of heterogeneous sensors and communication. Specifically, it is studied how to improve localization by exploring communication techniques as well as how to enhance communication performances by extracting information from perception sensors. The dynamic environment of multi-agent systems calls for robust, flexible and adaptive techniques, capable of profitably fuse different types of information, and the outcomes of these researches show how a statistical approach based on cooperation guarantees higher resilience, reliability and confidence.

scholarly journals DEVELOPMENT OF A MULTI-AGENT SYSTEM FOR THE ASSESSMENT OF ECOLOGICAL INFLUENCES OF TRANSPORT MEGAPROJECTS: TAKING INTO ACCOUNT THE INFORMATION ENVIRONMENT FACTOR

2020 ◽  
Vol 3 (1) ◽  
pp. 61-67
Author(s):  
Tatyana N. Yesikova ◽  
Svetlana V. Vakhrusheva
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
A Priori ◽  
Dynamic Environment ◽  
Information Environment ◽  
Information Space ◽  
Bering Strait ◽  
Multi Agent Systems ◽  
Multi Agent ◽  
Business Entities

The paper considers the issues of accounting and reflection in multi-agent systems of the influence of the information environment, information flows on agent behavior and the assessment of consequences, including environmental ones, of decisions made by them at various stages of large-scale infrastructure projects. The information space is a priori a multidimensional dynamic environment that is continuously updated and transformed, sometimes under the primacy of the interests of individual agents or influence groups, and much less frequently from the standpoint of ensuring the viability of the economic system as a whole. A large-scale project for the construction of a transcontinental highway (TKS) through the Bering Strait was chosen as the object of study. The article provides a fairly detailed description of the groups of agents involved in the decision-making process, as well as the elements of the information space that are significant for an agent at certain stages of its activity. To model the influence of the information space on decision-making processes by agents of different hierarchy levels (business entities, managerial entities, etc.), algorithms and special procedures have been developed.

Optimal Consensus Recovery of Multi-agent System Subjected to Agent Failure

2020 ◽  
Vol 29 (06) ◽  
pp. 2050017
Author(s):  
Deep Shekhar Acharya ◽  
Sudhansu Kumar Mishra
Keyword(s):  
Communication Channel ◽  
State Of The Art ◽  
Normal Operation ◽  
Multi Agent Systems ◽  
External Disturbances ◽  
Algorithmic Approach ◽  
Agent Based ◽  
Quick Recovery ◽  
Multi Agent ◽  
Agent Networks

Multi-Agent Systems are susceptible to external disturbances, sensor failures or collapse of communication channel/media. Such failures disconnect the agent network and thereby hamper the consensus of the system. Quick recovery of consensus is vital to continue the normal operation of an agent-based system. However, only limited works in the past have investigated the problem of recovering the consensus of an agent-based system in the event of a failure. This work proposes a novel algorithmic approach to recover the lost consensus, when an agent-based system is subject to the failure of an agent. The main focus of the algorithm is to reconnect the multi-agent network in a way so as to increase the connectivity of the network, post recovery. The proposed algorithm may be applied to both linear and non-linear continuous-time consensus protocols. To verify the efficiency of the proposed algorithm, it has been applied and tested on two multi-agent networks. The results, thus obtained, have been compared with other state-of-the-art recovery algorithms. Finally, it has been established that the proposed algorithm achieves better connectivity and therefore, faster consensus when compared to the other state-of-the-art.

Simulating UAV Surveillance for Analyzing Impact of Commitments in Multi-Agent Systems

2012 ◽  
Vol 4 (1) ◽  
pp. 1-16
Author(s):  
David C. Han ◽  
Suzanne K. Barber
Keyword(s):  
Autonomous Agents ◽  
Dynamic Environment ◽  
Action Selection ◽  
Best Interests ◽  
Multi Agent Systems ◽  
Rational Agents ◽  
Agent Systems ◽  
Negative Effect ◽  
Multi Agent ◽  
Selection Framework

Autonomous agents, by definition, have the freedom to make their own decisions. Rational agents execute actions that are in their “best interests” according to their desires. Action selection is complicated due to uncertainty when operating in a dynamic environment or where other agents can also influence the environment. This paper presents an action selection framework and algorithms that are rational with respect to multiple desires and responsive to changing desires. Coordination is layered on top of this framework by describing and analyzing how commitments affect the agents’ desires in their action selection models. Commitments may have a positive or a negative effect on an agent’s ability to satisfy its desires. This research uses simulation in the domain of UAV surveillance to experimentally explore the balance between under-commitment and over-commitment.

Swarm Intelligence Based Reputation Model for Open Multi agent Systems

2010 ◽  
pp. 248-266 ◽  
Author(s):  
Saba Mahmood ◽  
Azzam ul Asar ◽  
Hiroki Suguri ◽  
Hafiz Farooq Ahmad
Keyword(s):  
Swarm Intelligence ◽  
Multiagent Systems ◽  
Dynamic Environment ◽  
Sources Of Information ◽  
Multi Agent Systems ◽  
Interacting Agents ◽  
Utility Gain ◽  
Multi Agent ◽  
Trust Models ◽  
Reputation Model

In open multiagent systems, individual components act in an autonomous and uncertain manner, thus making it difficult for the participating agents to interact with one another in a reliable environment. Trust models have been devised that can create level of certainty for the interacting agents. However, trust requires reputation information that basically incorporates an agent’s former behaviour. There are two aspects of a reputation model i.e. reputation creation and its distribution. Dissemination of this reputation information in highly dynamic environment is an issue and needs attention for a better approach. We have proposed a swarm intelligence based mechanism whose self-organizing behaviour not only provides an efficient way of reputation distribution but also involves various sources of information to compute the reputation value of the participating agents. We have evaluated our system with the help of a simulation showing utility gain of agents utilizing swarm based reputation system. We have utilized an ant net simulator to compute results for the reputation model. The ant simulator is written in c

Multi-Agent Systems Research and Social Science Theory Building

2007 ◽  
pp. 101-110
Author(s):  
H. Verhagen
Keyword(s):  
Social Science ◽  
Organization Theory ◽  
Social Science Research ◽  
Science Research ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Research Areas ◽  
Systems Research ◽  
Science Theory ◽  
Multi Agent

This chapter describes the possible relationship between multi-agent systems research and social science research, more particularly sociology. It gives examples of the consequences and possibilities of these relationships, and describes some of the important issues and concepts in each of these areas. It finally points out some future directions for a bi-directional relationship between the social sciences and multi-agent systems research which hopefully will help researchers in both research areas, as well as researchers in management and organization theory.

Open Multi-Agent Systems for Collaborative Web-Based Learning

2007 ◽  
pp. 138-150
Author(s):  
Hongen Lu
Keyword(s):  
Service Providers ◽  
Dynamic Environment ◽  
Multi Agent Systems ◽  
Web Based ◽  
Education Services ◽  
Service Ontology ◽  
Web Based Learning ◽  
E Learning ◽  
Multi Agent

Web-based learning plays an important role in modern teaching environment. Many Web based tools are becoming available on this huge marketplace. Agent technology contributes substantially to this achievement. One of the fundamental problems facing both students and education services providers is how to locate and integrate these valuable services in such a dynamic environment. In this chapter, I present mediator-based architecture to build open multi-agent applications for e-learning. An agent services description language is presented to enable services advertising and collaboration. The language exploits ontology of service domain, and provides the flexibility for developers to plug in any suitable constraint languages. Multiple matchmaking strategies based on agent service ontology are given to help agents finding appropriate service providers. The series of strategies consider various features of service providers, the nature of requirements, and more importantly the relationships among services.

A Collaborative Multi-Agent Framework for Internet-Based Teleoperation Systems

2013 ◽  
Vol 5 (2) ◽  
pp. 31-54
Author(s):  
Nader Cheaib ◽  
Samir Otmane ◽  
Malik Mallem
Keyword(s):  
Conceptual Model ◽  
Multi Agent Systems ◽  
Collaborative Process ◽  
Agent Systems ◽  
Collaborative Software ◽  
Research Areas ◽  
Multi Agent ◽  
Collaboration Process ◽  
3C Model ◽  
Teleoperation Systems

This paper presents a conceptual model of an agent (called Collaborator Agent) intended to design collaborative software architectures based on multi-agent systems. The authors’ model combines astutely two research areas: Multi-Agent Systems (MAS) and Computer Supported Cooperative Work (CSCW). The particularity of their approach is the division of the collaborative process into three spaces according to Ellis' 3C model: communication, coordination and production. In their work, the authors extend the 3C model by adding a fourth space: collaboration. Hence, the authors present a model based on four types of agents (collaboration, communication, coordination and production) supporting the whole set of collaborative tasks. The model is used to create the conceptual software architecture of their MAS. The authors apply their conceptual model on the ARITI-C system for collaborative online robot teleoperation. Finally, the authors present a quantitative evaluation of the collaboration process in ARITI-C.

scholarly journals On Leader-Following Consensus in Multi-Agent Systems with Discrete Updates at Random Times

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 650
Author(s):  
Ricardo Almeida ◽  
Ewa Girejko ◽  
Snezhana Hristova ◽  
Agnieszka Malinowska
Keyword(s):  
Continuous Time ◽  
Sufficient Conditions ◽  
Control Law ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Leader Following ◽  
Multi Agent ◽  
Agent Networks ◽  
Random Times ◽  
Theoretical Results

This paper studies the leader-following consensus problem in continuous-time multi-agent networks with communications/updates occurring only at random times. The time between two consecutive controller updates is exponentially distributed. Some sufficient conditions are derived to design the control law that ensures the leader-following consensus is asymptotically reached (in the sense of the expected value of a stochastic process). The numerical examples are worked out to demonstrate the effectiveness of our theoretical results.

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