Generating and choosing organisations for multi-agent systems

This paper proposes a methodology for developing multi-agent systems. Several design considerations for robotic agents are discussed. A script specifies a multi-agent system by defining the behavior of each agent. An agent is modeled by an extended finite state automaton. By using the state transition rules as the building blocks, an agent as well as a multi-agent system can be developed efficiently. This methodology is further applied to multi-agent robotic systems in which reactivity and interaction are important issues. A script-based architecture is developed to implement the agents in multi-agent robotic systems. This architecture considers not only the control architecture of a single agent, but also the interactions among multiple agents and their environments. A script for the object-sorting task problem is utilized for demonstration.

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

Notice of Violation of IEEE Publication Principles<br><br>"Agents and Multi-agents Systems and Application to Condition Monitoring" by Christina B. Vilakazi, Tshilidzi Marwala<br>in the Proceedings of the 2007 International Conference on Systems, Man and Cybernetics 2007, pp. 644-649<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains significant methodology and text from the paper cited below. The content was used with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>"The Design of a Multi-Agent Transformer Condition Monitoring System"<br> by Stephen D.J. McArthur, Scott M. Strachan, and Gordon Jahn<br>in the IEEE Transactions on Power Systems, Vol 19, No 4, November 2004, pp. 1845-1852<br><br> <br/> In recent years the need for improved monitoring systems has necessitated the application of intelligent systems. In this study, a multi-agent system (MAS) is used for vibration condition monitoring. The system consists of functional agents such as feature extraction agent, interpretation agent, diagnosis agent, engineering assistant agent and registry agent. Support Vector Machine (SVM), Multi-layer perceptron (MLP) and Extension Neural Network (ENN) are used as interpretation agent. The rationale behind using multi-agent system for condition monitoring is explained. Experimental results show that multi-agent system is an effective artificial intelligence techniques for condition monitoring.

A generalized proportional Caputo fractional model of multi-agent linear dynamic systems via impulsive control protocol

The field of multi-agent dynamic systems is an inter-disciplinary research field that has become very popular in the recent years in parallel with the significant interest in the practical applications of such systems in various areas including robotics. In this article we give a relatively short review of this field from the system dynamics and control perspective. We first focus on mathematical modelling of multi-agent systems paying particular attention on the agent dynamics models available in the literature. Then we present a number of problems on coordination and control of multi-agent systems which have gained significant attention recently and various approaches to these problems. Relevant to these problems and approaches, we summarize some of the recent results on stability, robustness, and performance of multi-agent dynamic systems which appeared in the literature. The article is concluded with some remarks on the implementation and application side of the control designs developed for multi-agent systems.

Human social relationships imply conforming to the norms, behaviors, and cultural values of the society, but also socialization of emotions, to learn how to interpret and show them. In multiagent systems, much progress has been made in the analysis and interpretation of both emotions and norms. Nonetheless, the relationship between emotions and norms has hardly been considered and most normative agents do not consider emotions, or vice-versa. In this article, we provide an overview of relevant aspects within the area of normative agents and emotional agents. First we focus on the concept of norm, the different types of norms, its life cycle and a review of multiagent normative systems. Second, we present the most relevant theories of emotions, the life cycle of an agent’s emotions, and how emotions have been included through computational models in multiagent systems. Next, we present an analysis of proposals that integrate emotions and norms in multiagent systems. From this analysis, four relationships are detected between norms and emotions, which we analyze in detail and discuss how these relationships have been tackled in the reviewed proposals. Finally, we present a proposal for an abstract architecture of a Normative Emotional Agent that covers these four norm-emotion relationships.

Nowadays developing methods for distributed computing is received much attention. One of the methods of distributed computing is using of multi-agent systems. The organization of distributed computing based on the conventional network computers can experience security threats performed by computational processes. Authors have developed the unified agent algorithm of control system of computing network nodes operation. Network PCs is used as computing nodes. The proposed multi-agent control system for the implementation of distributed computing allows in a short time to organize using of the processing power of computers any existing network to solve large-task by creating a distributed computing. Agents based on a computer network can: configure a distributed computing system; to distribute the computational load among computers operated agents; perform optimization distributed computing system according to the computing power of computers on the network. The number of computers connected to the network can be increased by connecting computers to the new computer system, which leads to an increase in overall processing power. Adding multi-agent system in the central agent increases the security of distributed computing. This organization of the distributed computing system reduces the problem solving time and increase fault tolerance (vitality) of computing processes in a changing computing environment (dynamic change of the number of computers on the network). Developed a multi-agent system detects cases of falsification of the results of a distributed system, which may lead to wrong decisions. In addition, the system checks and corrects wrong results.

A New Approach In Learning For Intelligent Multi Agent Systems

As the complexity and scale of modern buildings increase, there is a growing need for efficient, scalable, and flexible control systems to manage the myriad of sensors, actuators, and meters involved in building automation. Traditional building management systems (BMS) struggle with scalability, especially when dealing with multi-agent control setups that require extensive manual configuration. This limitation poses significant challenges in deploying advanced control strategies across different building environments, leading to inefficiencies and underutilization of building automation technologies.Previous research has explored various aspects of multi-agent systems (MAS) in building automation, including agent-based modeling, decentralized control strategies, and the application of machine learning to optimize building operations. However, much of the existing work has focused on small-scale implementations or specific applications, such as energy management or HVAC control. The problem of automating the setup of multi-agent control systems to adapt to different building environments with minimal manual intervention remains largely unaddressed.The aim of this work is to develop a scalable framework for the automated setup of multi-agent control systems in buildings. This framework will enable the seamless integration of sensors, actuators, and meters into a unified control system, significantly reducing the need for manual configuration. The framework will leverage large language models (LLMs) for semantic tagging and context understanding, thereby facilitating the automated configuration of action and input spaces for individual agents within the control system.The proposed framework will consist of three key components: (1) an LLM-based system for semantic tagging and context interpretation, (2) an automated setup process for defining the action and input spaces of the agents, (3) and an initial offline training of the agents. The LLM will be trained on a diverse dataset of building management setups to ensure it can accurately tag and interpret various building elements. The system will be tested in real-world office buildings to evaluate its scalability, accuracy, and effectiveness.Preliminary simulations indicate that the automated setup framework can significantly reduce the time and effort required to deploy multi-agent control systems in buildings. We expect that this framework will enable more scalable and flexible building management systems, ultimately leading to improved energy efficiency, occupant comfort, and overall operational performance. The significance of this work lies in its potential to transform the deployment of building automation systems, making advanced control strategies more accessible and cost-effective across various building types and scales.

The MAS (Multi-Agent System) theory is already widely applied to the mechanical machining, especially the study on the robot. This paper puts forward a multi-Agent alliance control model that is made up of the series robot Agent, the parallel moving platform Agent and the polishing tool system Agent and so on by the MAS theory. Through the introduction of point-curve constraint, the dynamics of the constraint multi-rigid body equation for the polishing system is established in order to accurately program the moving track of the robots. Meanwhile, the control parameters during the polishing can be optimized. The satisfied polishing effect on the curved surfaces can be obtained and the demand of the polishing can be achieved by the MAS.

Developing A Multi-Agent System in JADE for Information Management in Educational Competence Domains

The issue of creating an information security system is very relevant in the world today. One of the urgent tasks is to solve the issues of effective protection of information from both external and internal threats through the creation and implementation of information security management systems in automated systems of enterprises, which, among other things, requires the formalization of the task of protecting information for its subsequent implementation by software and other means. Now there are security analysis systems, for example, that examine the security elements settings of workstations and servers operating systems, analyze the network topology, look for unprotected network connections, examine the settings of firewalls. The disadvantage of these systems is that they are not suitable for monitoring large volumes of network traffic. The solution to this problem is the use of monitoring tools capable of analyzing large amounts of data in real time. Therefore, a significant place in the article is given to the review of developments based on artificial intelligence technologies, namely multi-agent systems, review of information security models, threat risk assessment in automated systems. The functional architecture of the information security management system based on a multi-agent system has been proposed to search in real time for information security optimal solutions through the selection of such coalitions of protection mechanisms agents that will allow to build the optimal protection of the automated system according to the selected criteria. The model with complete overlapping of threats has been substantiated and adopted as a basis, which allows to analyze the overall situation and choose strategically important decisions directly during the organization of information security. The essence of of multi-agent systems functioning that implement a decentralized control system based on the work of autonomous agents that can be implemented programmatically has been revealed. The role of threat agents, resource agents, agents of protection mechanisms and their functional purpose have been defined. The problem of searching a set of protection mechanisms agents coalition for the current state of the automated system as a problem of optimal search by the criterion of protection cost, taking into account the value of information, has been generalized. Due to the modularity of the multi-agent system, the further work will be aimed at detailing its components and perfection.

Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instrum...

In cases where the success of artificial intelligence (AI) approaches is more focused on the multi-agent system (MAS) than those that may make AI understandable to humans, in light of the repetitive cycles of the history of AI, we expect to see technology reevaluation that is commonly referred to as 'classical IA' – especially agent and MAS – in the upcoming years. Other than that, agents and multi-agent (MAS) systems are crucial to designing intelligent systems from the start. They can open new possibilities to researchers for explanatory and intelligent systems through their long-term link with logical technology, which is characteristic of its early times. That is why it is now crucial to comprehend the present position of MAS for medical image methods. This paper thus seeks to provide a complete overview by reviewing the MAS and the agent approaches. Two different views are taken and assessed for the resulting technologies: the MAS and the image processing technologies.

The purpose of this chapter is to describe the application of the problem solving ontology component of HCVM in the area of face detection and annotation. computer-based aspects of an embedded feature-based multi-agent face detection and annotation system are described in this chapter. Unlike similar feature-based systems that start from searching for facial organs in the images and group them to find faces, the system solves the problem using the problem solving agents of the HCVM in a top-down fashion. The embedded mtilti-agent face detection system makes hypotheses of face locations and seeks evidences to verify them. The five problem solving agents follow a coarse grain to fine grain methodology and work on various features of color images. The coarse to fine top-down methodology is more akin to human perceptions than a bottom-up approach. The higher level problem solving agents like decomposition work as background knowledge for the control and decision agents resulting in improvement of the accuracy and speed of detection. It is also distinct from many other top-down systems in that no re-scaling of the images is needed in the searching process. To improve the detection rate, a unique iterative region-partitioning algorithm is developed in this multi-agent system. The problem solving agents model aspects related to skin-tone region segmentation, noise reduction, candidate face regions location and facial feature extraction/face detection, and annotation. The detection algorithm is invariant to scale and rotation to some degree. The performance of the multi-agent system has been tested with a large number of images and the results obtained thus far are very encouraging.