Intelligent Mechatronic System with Decentralised Control and Multi-Agent Planning

A New Approach In Learning For Intelligent Multi Agent Systems

A critical issue in patient planning is to determine whether the medical processes of a patient can be completed by a time constraint based on the available resources in hospitals. The problem is a Temporal Constraint Satisfaction Problem (TCSP). The objectives of this paper are to propose a viable and systematic approach to develop a distributed cooperative problem solver for TCSP and estimate the shortest and the longest completion time for handling a patient in the presence of uncertainty based on Multi-agent systems (MAS) architecture. Our approach combines MAS with a subclass of time Petri net (TPN) models to solve TCSP. Existing analysis methods of TPN based on state classes cannot be applied directly due to distributed architecture of MAS. In this paper, a temporal analysis method based on MAS architecture is proposed. Our temporal analysis method efficiently deduces the earliest and latest completion time of a patient based on interaction between agents.

This paper presents a software architecture for inter-organizational multiagent systems. The architecture integrates Web service technology into multiagent systems to overcome the technical interoperability problem of current multiagent systems in the fast growing service-oriented environments. We integrate Semantic Web technology to make multiagent systems semantically interoperable. We address the problem of interoperability regarding interfaces, messaging protocols, data exchanged, and security whilst considering a dynamic e-business environment. The proposed architecture enables service virtualization, secure service access across organizational boundaries, service-to-agent communication, and OWL reasoning within agents.

In the last few years, software applications have increased in complexity and in stakeholder’s expectations principally due to new Internet-centric application areas such as e-business, web services, ubiquitous computing, and peer-to-peer networks. Multi-agent systems (MAS) architectures have gained popularity for developing such software. Unfortunately, despite considerable work in software architecture during the last decade, few research efforts have aimed at truly defining frameworks for agent-based architectural design. Considering that a MAS architecture is conceived as a society of software agents, this paper overviews SKwyRL, a social-driven design framework dedicated to build up agent-based systems. The framework proposes a modern approach based on organizational structures and social patterns to define agent architectures notably in the context of e-business system design.

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

The Intrusion Detection System architectures used in commercial and research systems have a number of problems that limit their configurability. An important problem of agents: learning is not used. The concept of learning in existing IDSs used in general to learn the normal behavior of the system to secure. Thus, the IDS does not have the ability to detect new attacks. We propose in this paper a new architecture for intrusion detection based in multi agent systems adding a learning feature abnormal behaviors that correspond to new attack patterns. We present the motivation and description of the approach, for the detection step, the approach adopted is based on the technique of Case-Based Reasoning (CBR). The proposed architecture is based on a hierarchical and distributed strategy separated into three layers. We focus after on the modeling of our Multi agent systems Architecture, for reasons of simplicity, we use the methodology O-MaSE.

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.

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 escalating popularity of multi-agent systems makes it one of the promising technologies that provide the environment for developing collaborative intelligent software systems. We have proposed the light weight and fault tolerant architecture for multi-agent systems. SAGE-Lite is a lightweight context aware multi-agent system which senses the capabilities of the light-weight devices and reacts accordingly. Agents existing on lightweight devices can communicate and provide services via Bluetooth and the communication with the server is done via WAP. The intelligence may be pretty minimal but often will include some degree of learning from past experience. This paper addresses the issues of wireless environment and gives the detailed design of lightweight architecture context-aware light-weight multi-agent system of SAGE-Lite and its evaluation. The main objective is to allow light weight agents to participate in flexible communicating environment. As the system is based on the latest FIPA specifications, agent uses the FIPA-ACL for communication and bit-efficient protocol for encoding and decoding of ACL message

Recently, network security has become an extremely vital issue that beckons the development of accurate and efficient solutions capable of effectively defending our network systems and the valuable information journeying through them. In this article, a distributed multiagent intrusion detection system (IDS) architecture is proposed, which attempts to provide an accurate and lightweight solution to network intrusion detection by tackling issues associated with the design of a distributed multiagent system, such as poor system scalability and the requirements of excessive processing power and memory storage. The proposed IDS architecture consists of (i) the Host layer with lightweight host agents that perform anomaly detection in network connections to their respective hosts, and (ii) the Classification layer whose main functions are to perform misuse detection for the host agents, detect distributed attacks, and disseminate network security status information to the whole network. The intrusion detection task is achieved through the employment of the lightweight Adaptive Sub-Eigenspace Modeling (ASEM)-based anomaly and misuse detection schemes. Promising experimental results indicate that ASEM-based schemes outperform the KNN and LOF algorithms, with high detection rates and low false alarm rates in the anomaly detection task, and outperform several well-known supervised classification methods such as C4.5 Decision Tree, SVM, NN, KNN, Logistic, and Decision Table (DT) in the misuse detection task. To assess the performance in a real-world scenario, the Relative Assumption Model, feature extraction techniques, and common network attack generation tools are employed to generate normal and anomalous traffic in a private LAN testbed. Furthermore, the scalability performance of the proposed IDS architecture is investigated through the simulation of the proposed agent communication scheme, and satisfactory linear relationships for both degradation of system response time and agent communication generated network traffic overhead are achieved.

In the last few years, information system technologies have focused on solving challenges in order to develop distributed applications. Distributed systems can be viewed as collections of service-provider and service-consumer components interlinked by dynamically defined workflows (Luck and McBurney 2008). Nowadays, there has been a trend inmodeling software as a service in order to provide higher levels of functionality that facilitate the emergence of new services dynamically. These models demand complex, flexible, and adaptive systems, in which components cannot simply be passive and reactive entities managed by only one organization (Del Val et al. 2014). Instead of being a solitary activity, computation becomes an inherently social one, leading to new ways of conceiving, designing, developing, and handling computational systems (Sierra et al. 2011). Considering the emergence of distributed paradigms such as web services, serviceoriented computing, grid computing, peer-to-peer technologies, autonomic computing, etc., large systems can be viewed as the services that are offered and consumed by different entities. The concept of an intelligent agent provides support to build distributed systems as components with higher levels of intelligence, which demand complex ways of interaction and cooperation in order to solve specific objectives. Therefore, wide agreement can be found in the literature regarding the relevance of multiagent systems as a proper paradigm for building current and next generation distributed systems. Multiagent systems are one of the most important and exciting research areas that have arisen in the field of Information Technologies in the last decade (Luck et al. 2005). According to Wooldridge and Jennings (1995), an agent is defined by its flexibility, which implies that an agent is: reactive, an agent must answer to its environment; proactive, an agent has to be able to try to fulfill his own plans or objectives; and social, an agent has to be able to communicate with other agents by means of some kind of language. A multiagent system consists of a number of agents that interact with one another (Wooldridge 2002). The area of multiagent systems represents a key development issue, especially in distributed artificial intelligence. In open and dynamic systems, agents may enter or abandon the system for different reasons; agents act on behalf of service owners, managing access to services, and ensuring that contracts are fulfilled; agents act on behalf of service consumers, locating services, making contracts, and receiving and presenting results; agents are required to engage in interactions, negotiate with one another, make agreements, and make proactive run-time decisions, individually and collectively, while responding to changing circumstances; agents also need to collaborate within organizations and to form coalitions of agents with different capabilities in support of virtual organizations to reach global and individual goals. When developing applications based on the new generation of distributed systems, developers and users require infrastructures and tools that support essential features in multiagent systems (such as agent organizations, mobility, etc.) and that facilitate system design, management, execution, and J. M. Alberola (*) :V. Botti Departament de Sistemes Informatics i Computacio, Universitat Politecnica de Valencia, Valencia, Spain e-mail: jalberola@dsic.upv.es

User preference-based automatic orchestration of web services using a multi-agent

Epidemic spread poses a new challenge to the public health community. Given its very rapid spread, public health decision makers are mobilized to fight and stop it by setting disposal several tools. This ongoing research aims to design and develop a new system based on Multi-Agent System, Suscpetible-Infected-Removed (SIR) model and Geographic Information System (GIS) for public health officials. The proposed system aimed to find out the real and responsible factors for the epidemic spread and explaining its emergence in human population. Moreover, it allows to monitor the disease spread in space and time and provides rapid early warning alert of disease outbreaks. In this paper, a multi-agent epidemic spread simulation system is proposed, discussed and implemented. Simulation result shows that the proposed multi-agent disease spread system performs well in reflecting the evolution of dynamic disease spread system's behavior

A hybrid multi-agent system architecture for enterprise integration using computer networks

A paradigm known as multi-agent systems (MAS) combines several independent agents to accomplish intricate tasks. The creation of such systems presents difficulties with control flow, specialization, and modularity. With an emphasis on Lang-Graphbased implementation, this study examines various MAS topologies, such as network-based, supervisor-led, and hierarchical models. According to the results, MAS enhances performance and modularity while requiring careful inter-agent communication strategy design. This paper provides a thorough analysis of the architecture of Multi-Agent Systems (MAS), emphasizing its fundamental ideas, constituent parts, and uses. In the advanced distributed computing paradigm known as MAS, autonomous agents work together to resolve challenging issues. In “order to shed light on the possibilities of MAS in a number of fields, including as robotics, e-commerce, transportation, and healthcare, this study examines agent definitions, system architecture, communication protocols, decision-making methods, and practical applications. Autonomous agents are integrated in Multi-Agent Systems (MAS) to address complex issues, although control flow, specialization, and modularity present difficulties. This paper examines three topologies-network-based, supervisorled, and hierarchical-detailing inter-agent communication protocols and coordination mechanisms with an emphasis on Lang-Graph-based implementations. Each architecture is demonstrated with sample code, and the findings demonstrate that, with well-thought-out communication mechanisms, MAS improves performance and modularity. Applications include e-commerce, healthcare, transportation, and robots.