The Information Flow Problem in multi-agent systems

Búrdalo, Luis; Terrasa, Andrés; Julián, Vicente; García-Fornes, Ana

doi:10.1016/j.engappai.2018.01.011

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…An application of our framework that would be interesting to investigate is its use to reduce the needs to coordinate multiple assistive agents advising the same user [16]. Other possible applications would be in the case of the Information Flow Problem in multi-agent systems, in which there is a need to ensure an adequate exchange of information within a system [3], and in the case of BDI personal medical assistant agents, where one critical requirement is to (automatically) produce an accurate documentation [6].…”

Section: Discussionmentioning

confidence: 99%

Usefulness of Information for Goal Achievement

Cholvy

Pereira

2019

PRIMA 2019: Principles and Practice of Multi-Agent Systems

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This paper focuses on modelling information usefulness. More precisely, it aims at characterizing how useful a piece of information is for a cognitive agent which has some beliefs and goals. The paper presents three different approaches. We take Information Retrieval as a particular application domain and we compare some existing measures with the usefulness measure introduced in the paper.Célia da Costa Pereira acknowledges support of the PEPS AIRINFO project funded by the CNRS. This work has been carried out during her visit at the ONERA center of Toulouse.

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Section: Discussionmentioning

confidence: 99%

Usefulness of Information for Goal Achievement

Cholvy

Pereira

2019

PRIMA 2019: Principles and Practice of Multi-Agent Systems

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“…To design an effective MAS control mechanism, it is necessary to pay attention to the information flow between agents that can be defined in the forms of its message passing platform, information sharing level, and communication failures over time [94]. Information flow in an adaptive multi-agent network is implemented either for collecting agent status and outcomes and sending them to controllers or for delivering commands from controllers to interacting agents.…”

Section: Information Flowmentioning

confidence: 99%

“…Some of the agents have minimum information-sharing regarding expected rewards, agent configuration, or adaptation tactics [28]. Although in most of the existing literature it is assumed that agents are only able to communicate with their neighboring agents, some studies targeted other types of peerto-peer communications between agents, such as broadcasting or communications through middle agents [94]. The middleware can be a matchmaker that manages and matches the right information to the right agent or a broker that filters or rewords the communicated information and then distributes it to the related agents.…”

Section: Information Flowmentioning

confidence: 99%

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

Nezamoddini

Gholami

2022

Smart Cities

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The world is moving toward a new connected world in which millions of intelligent processing devices communicate with each other to provide services in transportation, telecommunication, and power grids in the future’s smart cities. Distributed computing is considered one of the efficient platforms for processing and management of massive amounts of data collected by smart devices. This can be implemented by utilizing multi-agent systems (MASs) with multiple autonomous computational entities by memory and computation capabilities and the possibility of message-passing between them. These systems provide a dynamic and self-adaptive platform for managing distributed large-scale systems, such as the Internet-of-Things (IoTs). Despite, the potential applicability of MASs in smart cities, very few practical systems have been deployed using agent-oriented systems. This research surveys the existing techniques presented in the literature that can be utilized for implementing adaptive multi-agent networks in smart cities. The related literature is categorized based on the steps of designing and controlling these adaptive systems. These steps cover the techniques required to define, monitor, plan, and evaluate the performance of an autonomous MAS. At the end, the challenges and barriers for the utilization of these systems in current smart cities, and insights and directions for future research in this domain, are presented.

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“…For the MAS, the major problem is that there are considerable information exchanges in the system, which is not only associated with the availability of highly efficient and sophisticated message-passing mechanisms actually provided by current multi-agent platforms, but also with the selection of an appropriate communication strategy [4]. Coordination of actions and plans that should be achieved by multiple agents is one of the most arduous tasks in the multi-agent domain [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-agent system application in accordance with game theory in bi-directional coordination network model

Zhang

Wang

Yue

et al. 2020

J. of Syst. Eng. Electron.

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The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also, as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop. Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.

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The Information Flow Problem in multi-agent systems

Cited by 10 publications

References 31 publications

Usefulness of Information for Goal Achievement

Usefulness of Information for Goal Achievement

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

Multi-agent system application in accordance with game theory in bi-directional coordination network model

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