Multi-agent Architecture Research Articles

Multi-Agent Architecture of Intelligent and Distributed Platform of Governance, Risk and Compliance of Information Systems

Governance, risk management and compliance of information technologies (IT GRC) is the responsibility of the company’s executives. The IT GRC responds to the important concerns of information systems managers, to ensure the necessary changes in the Information System (IS) over time, and enable it to meet the needs of risk mitigation, regulatory compliance, value creation and strategic alignment. Like a large number of organizations' activities, the IT GRC has to find a solution that is equipped through IS applications. Although these tools do exist, they are never developed by considering the IT GRC processes as a whole. We respond to this lack of consideration by proposing an intelligent and distributed platform of risk, governance and compliance of information systems that deploys a variety of IT GRC best practices and frameworks and makes an intelligent choice under constraints and parameters of the best framework to evaluate the objectives and processes in question. EAS-COM (communication system dedicated to the IT GRC platform) is our second proposal in this work: it ensures end-to-end communication between the different layers of the proposed IT GRC platform. This approach is based on Multi-Agent System (MAS) intelligence to manage the interactions between the distributed systems of the IT GRC platform.

A multi-level generic multi-agent architecture for supervision of collective cyber-physical systems

Cyber-physical systems like networked embedded systems or ambient sensors networks are called large-scale artificial complex systems. They are difficult to supervise because of their numerous components in interaction relying upon physical devices, their extension and their openness. A multi-scale organisation can be a solution to make them more accessible. Multi-agent systems (MAS) are well suited for modeling large complex systems, as multi-agent organisational capabilities allow introducing multi-level observation. This paper proposes a multi-level multi-agent mechanism based on recursion for supervision and observation of large-scale artificial complex systems. It is developed as a free framework that is a decentralised application allowing truly physically decentralised MAS to communicate with abstract multi-agent layers. As an illustration, the framework is applied to a wireless sensor network supervision system.

Architecture to Embed Software Agents in Resource Constrained Internet of Things Devices.

Sensing systems in combination with treatment tools and intelligent information management are the basis on which the cities and urban environments of the future will be built. Progress in the research and development of these new and intelligent scenarios is essential to achieve the objectives of efficiency, integration, sustainability, and quality of life for people who live in cities. To achieve these objectives, it is essential to investigate the development of cheaper, more accurate, and smarter hardware devices, which will form the basis of the intelligent environments of the future. This article focuses on an approach based on intelligent multi-agent systems that are integrated into basic hardware devices for the Internet of Things (IoT). A multi-agent architecture is proposed for the fast, efficient, and intelligent management of the generated data. A layer of services adapted to the needs of the new intelligent environments is built on this architecture. With the aim of validating this architecture, a case study based on electric vehicles of the e-bike type is proposed.

An immune network based distributed architecture to control public bus transportation systems

Although many Public Transportation Control Systems (PTCS) were developed to support the management of public transportation systems, there is still a need to improve the capability of PTCS to deal with a variety of disturbance types, which may degrade performance (e.g. earliness/delay) and quality of service (QoS, e.g. punctuality, frequency and utilization). This paper extends our previous works on artificial immune systems to control Public Transportation Systems by means of buses (PTS). The main contribution consists in developing a distributed reaction mechanism based on the immune network theory and its related mechanisms, such as cell co-stimulation, co-suppression, activation and coordination. The immune network reaction mechanism is implemented within a distributed multi-agent architecture, and its performance is assessed using traffic simulation software. The immune network reaction mechanism is benchmarked against other control strategies (holding at station, skip station, and their combination), and performance is evaluated based on three classes of criteria: time oriented criteria, passenger oriented criteria and service oriented criteria. The performance of the suggested system with respect to passenger and service oriented criteria was remarkably higher than the performance of other control algorithms. With respect to time oriented criteria, the performance of the suggested system was found slightly better than the combination of holding and skipping stations.

Solving Traffic Routing System using VANet Strategy Combined with a Distributed Swarm Intelligence Optimization

Proposing an efficient strategy to reduce traffic congestion is an essential step towards improvement as we take into consideration the unpredictable and dynamic infrastructure of the road network. With the advances in computing technologies and communications protocols, we can retrieve any type of data and receive in real-time the state of traffic congestion at each road using Electronic Toll Collection System (ETCS), Vehicle Traffic Routing System (VTRS), Intelligent Transportation System (ITS) and Traffic Light Signals (TLS). This study introduces a new distributed strategy that aims to optimize traffic road congestion in real-time based on the Vehicular Ad-Hoc Network (VANET) communication system and the techniques of the Ant Colony Optimization (ACO). The VANET is used as a communication technology that will help us create a channel of communication between several vehicles and routes. The techniques of the ACO is used to compute the shortest path that can be followed by the driver to avoid congested routes. The proposed system is based on a multi-agent architecture, in which all agents work together to monitor the road traffic congestion and help drivers quickly arrive at their destinations by following the best routes with less congestion. Simulation results show that the proposed method can reduce the total distance traveled and time taken in order to reach a destination, as compared to the classic “shortest path method” (based only on the distance).

A Hybrid Intelligent Multiagent System for the Remote Control of Solar Farms

ABSTRACTThis paper describes a multiagent architecture integrated system designed to supervise infrastructures in solar farms. The system enables monitoring the environment by means of sensor networks that are in charge of collecting data. It is designed using a hybrid model composed of an inference engine and an ontology. The former makes the system intelligent, while the latter structures knowledge. We have also developed a tool to configure and use the multiagent system in a simple and intuitive way.

Real-Time Path-Generation and Path-Following Using an Interoperable Multi-Agent Framework

Autonomous unmanned vehicles are preferable in patrolling, surveillance and, search and rescue missions. Multi-agent architectures are commonly used for autonomous control of unmanned vehicles. Existing multi-robot architectures for unmanned aerial and ground robots are generally mission and platform oriented. Collision avoidance, path-planning and tracking are some of the fundamental requirements for autonomous operation of unmanned robots. Though aerial and ground vehicles operate differently, the algorithms for obstacle avoidance, path-planning and path-tracking can be generalized. Service Oriented Interoperable Framework for Robot Autonomy (SOIFRA) proposed in this work is an interoperable multi-agent framework focused on generalizing platform independent algorithms for unmanned aerial and ground vehicles. SOIFRA is behavior-based, modular and interoperable across unmanned aerial and ground vehicles. SOIFRA provides collision avoidance, and, path-planning and tracking behaviors for unmanned aerial and ground vehicles. Vector Directed Path-Generation and Tracking (VDPGT), a vector-based algorithm for real-time path-generation and tracking, is proposed in this work. VDPGT dynamically adopts the shortest path to the destination while minimizing the tracking error. Collision avoidance is performed utilizing Hough transform, Canny contour, Lucas–Kanade sparse optical flow algorithm and expansion of object-based time-to-contact estimation. Simulation and experimental results from Turtlebot and AR Drone show that VDPGT can dynamically generate and track paths, and SOIFRA is interoperable across multiple robotic platforms.

Decision Support for Negotiations among Microgrids Using a Multiagent Architecture

This paper presents a decision support model for negotiation portfolio optimization considering the participation of players in local markets (at the microgrid level) and in external markets, namely in regional markets, wholesale negotiations and negotiations of bilateral agreements. A local internal market model for microgrids is defined, and the connection between interconnected microgrids is based on nodal pricing to enable negotiations between nearby microgrids. The market environment considering the local market setting and the interaction between integrated microgrids is modeled using a multi-agent approach. Several multi-agent systems are used to model the electricity market environment, the interaction between small players at a microgrid scale, and to accommodate the decision support features. The integration of the proposed models in this multi-agent society and interaction between these distinct specific multi-agent systems enables modeling the system as a whole and thus testing and validating the impact of the method in the outcomes of the involved players. Results show that considering the several negotiation opportunities as complementary and making use of the most appropriate markets depending on the expected prices at each moment allows players to achieve more profitable results.

Dynamic configuration and collaborative scheduling in supply chains based on scalable multi-agent architecture

Due to diversified and frequently changing demands from customers, technological advances and global competition, manufacturers rely on collaboration with their business partners to share costs, risks and expertise. How to take advantage of advancement of technologies to effectively support operations and create competitive advantage is critical for manufacturers to survive. To respond to these challenges, development of a dynamic scheme to better manage collaborative workflows is urgent. In this paper, we will study how to develop a flexible and scalable framework to dynamically and coherently configure workflows that can meet order requirements based on multi-agent systems (MAS). Configuring and scheduling collaborative workflows is a challenging problem due to the computational complexity involved, distributed architecture and dependency among different partners’ workflows. To achieve flexibility and reduce the cost and time involved in configuration of a supply chain network, we propose an approach that combines MAS, contract net protocol, workflow models and automated transformation of the workflow models to dynamically formulate the scheduling problem. To attain scalability, we develop a solution algorithm to solve the optimization problem by a collaborative and distributed computation scheme. We implement a software system based on industrial standards, including FIPA and the Petri net workflow specification model. In addition, we also illustrate effectiveness and analyze scalability of our approach by examples. Our approach facilitates collaboration between partners and provides a scalable solution for the increasing size of supply chain networks.

A systematic approach for supporting the adaptation process of discrete manufacturing machines

Automated manufacturing machines in the discrete manufacturing domain frequently face changes in requirements, such as volatile customer demands or changes in product variants. Due to this, machines need to become more flexible to cope with these changing conditions. Therefore, manufacturing machines have to undergo adaptation processes during their operational phase. The adaptation processes might include mechanical, electrical, and software changes. In industrial practice, experts individually perform these adaptation processes without methodological support, which is time-consuming and highly error-prone. This article proposes a systematic approach for supporting the different phases of the adaptation process. The producibility check of a production request based on a suitable skill model of the system is addressed as well as the automatic generation of adaptation options. Furthermore, the article provides concepts for analyzing the impact, effort and benefit of the generated adaptation options. Additionally, a multi agent architecture is presented for the implementation of the proposed adaptation approaches. The entire assistance concept was applied to a lab-size production machine to validate the applicability of the approach.

Help System for Creating Educational Resources for Arabic

In this article, the authors have discussed the implementation of a CALL system for Arabic-based resources from the automatic language processing, and the learning environment “TELA,” which attempt to bring new technological ways to call. This work presents the advantages of the computer's use as a tool for teaching and learning the Arabic language. The first part of this thesis is the development of a set of linguistic resources, such as a multifunctional dictionary (as complete as possible), based on a multi-agent architecture and has morphological analysis tool “TELAMA.” These objectives were achieved with resources, methods and effective approaches in different parts of this research. The second part is the integration of these resources into “TELA” in order to provide teachers with interactive and more possible finished tools to generate varied and automated educational activities enabling learners to learn the Arabic language.

New Power-Oriented Methodology for Dynamic Resizing and Mobility of Reconfigurable Wireless Sensor Networks

This paper deals with reconfigurable wireless sensor networks (RWSNs) that should be adapted to their environment in order to minimize the energy consumption during the communication among nodes and consequently to maximize the lifetime of the network as much as possible. An RWSN is assumed to be composed of a set of nodes located in distributed zones such that each node executes reconfigurable software tasks to control local sensors. We propose in a previous work a zone-based multiagent architecture for an RWSN where a communication protocol is well-defined to optimize distributed reconfigurations. This architecture combines all possible reconfiguration forms of the network to be adapted to its environment under energy constraints. With the multiagent architecture we gain in term of energy to be consumed by each node in the network. After applying a set of reconfiguration scenarios, the total charge of the network decreases. In order to preserve this charge as much as possible before the next recharging operation, in this particular study, we tend to resize geographically the zones in an RWSN. We propose also to move the mobile nodes in order to increase the network lifetime. A new run-time power oriented methodology is reported, which manages the zones and their mobile nodes in order to control the energy consumption. Two dynamic solutions are applied: 1) the resizing of zones and 2) the mobility of nodes. For the new proposed methodology, we present a set of equation systems to model the resizing of zones and the mobility of nodes. The solution of these systems allows us to conclude that we can conserve more energy during the communication among network elements (nodes, stations, and agents). The contributions are applied to a case study that we simulate with the reconfigurable wireless network environment 1 to analyze the research originality. 1 http://lisi-lab.wix.com/rwinproject

A multi-agent framework for cloud-based management of collaborative robots

This article presents a cloud-based multi-agent architecture for the intelligent management of aerial robots in a disaster response situation. In a disaster scenario, a team of highly maneuverable quadcopters is deployed to carry out surveillance and decision support in disaster-affected areas. In Pakistan, such events usually result from sudden unpredictable calamities such as earthquakes. The aim of this work is to develop a robust mechanism to autonomously manage and react to sensory inputs received in soft real time from an unstructured environment. The immediate goal is to locate the maximum number of trapped, injured people within a large area, and help first responders plan rescue activities accordingly. To evaluate the proposed framework, a number of simulations are carried out using GAMA platform to emulate a disaster environment. Subsequently, algorithms are developed to survey an affected geographical area through the use of small flight drones. The key challenges in this work are related to the combination of the domains of multi-agent technology, robotics, and cloud computing for effectively bridging the cyber world with the physical world. Therefore, the proposed work demonstrates the effective use of a limited number of drones to capture inputs from a disaster situation in the physical world, and such inputs are used for timely planning of rescue efforts. The results of fixed resource assignment are compared with the proposed reactive assignment strategy, and it clearly shows a significant improvement in terms of resource usage compared to traditional approach.

Freight transportation broker agent based on constraint logic programming

We propose an agent-based freight brokering system that provides an intelligent logistics brokerage service focusing on the transport activity for the efficient allocation of transport resources (vehicles or trucks) to the transport applications. The freight broker has the role to coordinate transportation arrangements of transport customers with transport resource providers. In this paper we focus on the fundamental function of this business that aims to find available trucks and to define their feasible routes for transporting requested customer loads. Our main achievement is the proposal of a knowledge-based freight broker based on agents and constraint programming. We describe the multi-agent architecture and the interaction protocol of the freight broker. The brokering function is defined as a special type of vehicle routing with pickup and delivery problem. This function is achieved by proposing an optimization agent based on constraint logic programming. We present in detail our novel declarative optimization model, as well as the components of the optimization agent. This model was implemented and evaluated using the state-of-the-art ECLiPSe-CLP engine. The experimental results that we obtained for sample benchmark problems are promising and they support the effectiveness of our approach in a practical setting.

Process mining of a multi-agent business simulator

A multi-agent system is a useful modeling architecture in business process modeling in the sense that we can naturally implement participants in a real company with software agents. However, analyzing and interpreting the simulation results of multi-agent models tends to be difficult due to the inherent complexity of the models. In this regard, another discipline—process mining—is useful for such purposes because it has demonstrated its usefulness in analyzing real processes. In this article, our aim is to combine these two disciplines for exploitation in business process modeling and simulation; we extend a multi-agent-based business simulator named Multi-Agent system with Resource-Event-Agent ontology (MAREA) to be able to be analyzed by means of process mining techniques. To this end, we formalize the abstract multi-agent architecture of MAREA and establish its relationship to process mining by defining how execution of a multi-agent system can be recorded as an event log, which is later analyzed by process mining techniques. Based on this definition, we implement functionality to extract event logs from simulation runs in MAREA. For demonstration, we implement a model of a generic trading company in MAREA and perform process structure verification and social network analyzes by means of process mining techniques.

Real-time Multi-Agents Architecture for E-commerce Servers

Electronic commerce applications have strict timing constraints on the interactions between e-commerce servers and customers. Customers prefer real-time services, which mean immediate response from the server shortly after submission of a request. This trend of demand gives e-commerce servers high pressure, both on the software and the architecture they currently use. In this paper, we propose a real-time architecture for e-commerce servers that addresses the problem efficiently. We adopt a framework that permits the appropriate treatment of dynamic behaviours that are data interdependent, and reasoning about the communication protocols and internal mechanisms of client / server relationships in a real-time multi-agents based e-commerce application architecture.

Energy Optimization Using a Case-Based Reasoning Strategy.

At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.

Co-simulation of cyber-physical systems using a DEVS wrapping strategy in the MECSYCO middleware

Most modeling and simulation (M&S) questions about cyber-physical systems (CPSs) require expert skills belonging to different scientific fields. The challenges are then to integrate each domain’s tools (formalism and simulation software) within the rigorous framework of M&S process. To answer this issue, we give the specifications of the Multi-agent Environment for Complex-SYstem CO-simulation (MECSYCO) middleware which enables to interconnect several pre-existing and heterogeneous M&S tools, so they can simulate a whole CPS together. The middleware performs the co-simulation in a parallel, decentralized, and distributable fashion thanks to its modular multi-agent architecture. In order to rigorously integrate tools that use different formalisms, the co-simulation engine of MECSYCO is based on the discrete event system specification (DEVS). The central idea of MECSYCO is to use a DEVS wrapping strategy to integrate each tool into the middleware. Thus, heterogeneous tools can be homogeneously co-simulated in the form of a DEVS system. By using DEVS, MECSYCO benefits from the numerous scientific works which have demonstrated the integrative power of this formalism and give crucial guidelines to rigorously design wrappers. We demonstrate that our discrete framework can integrate a vast amount of continuous M&S tools by wrapping the Functional Mockup Interface (FMI) standard. To this end, we take advantage of DEVS efforts of the literature (namely, the DEV&DESS hybrid formalism and Quantized State System (QSS) solvers) to design DEVS wrappers for Functional Mockup Unit (FMU) components. As a side-effect, this wrapping is not restricted to MECSYCO but can be applied in any DEVS-based platform. We evaluate MECSYCO with the proof of concept of a smart heating use case, where we co-simulate non-DEVS-centric M&S tools.

A Multi Agent Based Organizational Architecture for Dynamic Pickup and Delivery Problem

Pickup and Delivery Problem (PDP) consists of searching an optimal set of vehicles and an optimal set of routes, one route by each vehicle, in order to pickup items from a set of origins and deliver them to another set of destinations. Pickup and delivery problem is a class of complex systems whose complexity is NP Hard. In PDP real life applications, heuristics and meta heuristics methods are used in order to obtain an acceptable solution in reasonable execution time. When unpredictable events, like for example path cut and vehicles failure, may occur during the PDP schedule execution, we say that the PDP is dynamic (DPDP) and in this case we have to revise this schedule. In this paper, we propose a multi agent architecture for DPDP based on an organizational architecture. Supported by a formal framework, the proposed architecture allows us to show, through a case study that computed solution for the studied problem could be done in a parallel manner which attenuates substantially the problem complexity.

Combination of Multi-Agent Systems and Wireless Sensor Networks for the Monitoring of Cattle.

Precision breeding techniques have been widely used to optimize expenses and increase livestock yields. Notwithstanding, the joint use of heterogeneous sensors and artificial intelligence techniques for the simultaneous analysis or detection of different problems that cattle may present has not been addressed. This study arises from the necessity to obtain a technological tool that faces this state of the art limitation. As novelty, this work presents a multi-agent architecture based on virtual organizations which allows to deploy a new embedded agent model in computationally limited autonomous sensors, making use of the Platform for Automatic coNstruction of orGanizations of intElligent Agents (PANGEA). To validate the proposed platform, different studies have been performed, where parameters specific to each animal are studied, such as physical activity, temperature, estrus cycle state and the moment in which the animal goes into labor. In addition, a set of applications that allow farmers to remotely monitor the livestock have been developed.