Multi-Robot System Based on Swarm Intelligence for Optimal Solution Search

Ababii, Victor; Sudacevschi, Viorica; Braniste, Rodica; Ana, Nistiriuc; Munteanu, Silvia; Borozan, Olesea

doi:10.1109/hora49412.2020.9152926

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…is the set of objective functions for quality assessment for the service defined by the set of state parameters The process of finding the optimal solution for the quality of service is performed based on game theory with Nash Equilibrium condition [21,22]. The process of service generation in Smart City is a competitive decision-making process between the set of Service Providing Agents and the set of Service Consuming Agents.…”

Section: The Model For Multi-objective Search Of the Optimal Quality ...mentioning

confidence: 99%

Decision-Making Support System for Quality Smart City Services

Ababii,

Sudacevschi,

Munteanu

et al. 2023

IJPSAT

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This paper presents the results of research carried out in the field of developing decision support systems for quality Smart City services. The decision-making system consists of two sets of Agents: Service Provider Agents and Service Consumer Agents. The interaction between the Agent sets is governed by the knowledge base which is managed by the Service Quality Assessors. Service quality is evaluated based on a Multi-Objective Optimization model competitively performed by applying game theory (Nash Equilibrium) between Agent sets involving available resources and knowledge. The paper developed: interaction diagram between Agents and services offered by Smart City, interaction diagram between sets of Agents to provide quality services, and multi-level infrastructure diagram for decision support system. The Multi-Objective Optimization problem is defined in the form of the set of objective functions for the evaluation of service quality and the set of constraint functions for service state parameters and action parameters for service provision.

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Section: The Model For Multi-objective Search Of the Optimal Quality ...mentioning

confidence: 99%

Decision-Making Support System for Quality Smart City Services

Ababii,

Sudacevschi,

Munteanu

et al. 2023

IJPSAT

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“…Modern technologies offer a wide range of solutions that allow the development of distributed computing decision making systems that ensure the solution of complex problems for different application areas. It can be mentioned papers: [13,14,17,19] in which were researched Multi-Agent systems oriented towards collective calculation, and [15,16,18] where cognitive systems based on knowledge with application in various fields are researched.…”

Section: Statement Of the Research Problemmentioning

confidence: 99%

Multi-Agent System for Decision Making Based on Temporal Logic

Alexei¹,

Munteanu²,

Borozan³

et al. 2022

Proceedings of the 11th International Conference on “Electronics, Communications and Computing (IC|ECCO-2021)”

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The paper presents the results of the conceptual and structural design of a Multi-Agent system for decision making based on temporal logic. The decision making system has a Multi-Agent structure that forms a mesh network with broadcast communication, which ensures the organization o knowledge exchange between them. Functional elements and temporal logical operators are defined in the form of mathematical models, which allows their implementation based on hardware devices or software products. The functionality of the temporal logic is determined by the time function that calculates the credibility coefficient of the event and its influence on the decisions taken by the Agents

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“…The functional model [22,24] of the Service Delivery Agent is defined by the expression (2): where: 𝑘𝑘 𝑆𝑆𝑆𝑆[𝑇𝑇] -the request coefficient for the services offered by SDA for SCA; 𝐾𝐾𝐾𝐾[𝑇𝑇] -the knowledge [20,23] base available to the SDA agent at the time 𝑇𝑇; 𝑆𝑆𝑆𝑆 -data processing logic applied by the SDA agent; 𝐸𝐸𝐸𝐸[𝑇𝑇] -the set of events used by the SDA agent for data processing for the purpose of delivering quality services to SCA agents; 𝑇𝑇𝑇𝑇𝐺𝐺[𝑇𝑇] -temporal logic operators used for data processing; 𝑆𝑆𝑇𝑇𝐺𝐺[𝑇𝑇] -spatial logic operators used for data processing; 𝑆𝑆𝐸𝐸[𝑇𝑇]the multitude of services offered by the SDA agent at the time 𝑇𝑇.…”

Section: Service Delivery Agent Synthesismentioning

confidence: 99%

“…The economic model of the Smart City Service System can be interpreted as a model of economic relations defined by game theory (Nash equilibrium) [16,24,25], in which two sets of agents are involved: SDA and SCA.…”

Section: Economic Model Of the Smart City Service Systemmentioning

confidence: 99%

Smart City Services Based on Spatial-Temporal Logic

Turcan¹,

Ababii²,

Sudacevschi³

et al. 2022

JES

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The development and research of Smart City Service Systems is a very important area for the future of mankind. The urbanization process imposes new criteria for qualitative and quantitative assessment of population well-being, which will involve processing a very large volume of information, organizing the data exchange and processing. This paper proposes a Multi-Agent Smart City Services system based on Spatial-Temporal logic. In order to optimize the criteria for the qualitative and quantitative evaluation of services, the set of agents is divided into: the subset of agents that deliver services and the subset of service consumers agents. The system diagram, the synthesis of the agents, the operators of temporal and spatial logic was elaborated. The relationship between the subset of service delivery agents and the subset of agents of service consumers is determined by game theory models.

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Multi-Robot System Based on Swarm Intelligence for Optimal Solution Search

Cited by 6 publications

References 9 publications

Decision-Making Support System for Quality Smart City Services

Decision-Making Support System for Quality Smart City Services

Multi-Agent System for Decision Making Based on Temporal Logic

Smart City Services Based on Spatial-Temporal Logic

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