Practical applications of multi-agent systems in electric power systems

The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur

Swarm-inspired solution strategy for the search problem of unmanned aerial vehicles

Learning from the emergent behaviour of social insects, this research studies the influences of environment to collective problem-solving of insect behaviour and distributed intelligent systems. Literature research has been conducted to understand the emergent paradigms of social insects, and to investigate current research and development of distributed intelligent systems. On the basis of the literature investigation, the environment is considered to have significant impact on the effectiveness and efficiency of collective problem-solving. A framework of collective problem-solving is developed in an interdisciplinary context to describe the influences of the environment to insect behaviour and problem-solving of distributed intelligent systems. The environment roles and responsibilities are transformed into and deployed as a problem-solving mechanism for distributed intelligent systems. A swarm-inspired search strategy is proposed as a behaviour-based cooperative search solution. It is applied to the cooperative search problem of Unmanned Aerial Vehicles (UAVs) with a series of experiments implemented for evaluation. The search environment represents the specification and requirements of the search problem; defines tasks to be achieved and maintained; and it is where targets are locally observable and accessible to UAVs. Therefore, the information provided through the search environment is used to define rules of behaviour for UAVs. The initial detection of target signal refers to modified configurations of the search environment, which mediates local communications among UAVs and is used as a means of coordination. The experimental results indicate that, the swarm-inspired search strategy is a valuable alternative solution to current approaches of cooperative search problem of UAVs. In the proposed search solution, the diagonal formation of two UAVs is able to produce superior performance than the triangular formation of three UAVs for the average detection time and the number of targets located within the maximum time length

EXODUS: Integrating intelligent systems for launch operations support

Kennedy Space Center (KSC) is developing knowledge-based systems to automate critical operations functions for the space shuttle fleet. Intelligent systems will monitor vehicle and ground support subsystems for anomalies, assist in isolating and managing faults, and plan and schedule shuttle operations activities. These applications are being developed independently of one another, using different representation schemes, reasoning and control models, and hardware platforms. KSC has recently initiated the EXODUS project to integrate these stand alone applications into a unified, coordinated intelligent operations support system. EXODUS will be constructed using SOCIAL, a tool for developing distributed intelligent systems. EXODUS, SOCIAL, and initial prototyping efforts using SOCIAL to integrate and coordinate selected EXODUS applications are described

Важлива складова надійності залізничного транспорту (Нове у системі моніторингу рейок і коліс)

У статті проаналізовано нові підходи до якісного контролю стану залізничного полотна і рухомого складу. Автор пропонує нові контрольні системи і вимірювальні прилади з використанням розподілених інтелектуальних мереж Lon Works.New approaches to the qualitative monitoring of a condition of rails and wheels are analysed in the article. Author proposes the new control and monitoring systems and measurement equipments with distributed intelligent systems Lon Works usage

Middleware-based multi-agent development environment for building and testing distributed intelligent systems

The spread of the Internet of Things (IoT) is demanding new, powerful architectures for handling the huge amounts of data produced by the IoT devices. In many scenarios, many existing isolated solutions applied to IoT devices use a set of rules to detect, report and mitigate malware activities or threats. This paper describes a development environment that allows the programming and debugging of such rule-based multi-agent solutions. The solution consists of the integration of a rule engine into the agent, the use of a specialized, wrapping agent class with a graphical user interface for programming and testing purposes, and a mechanism for the incremental composition of behaviors. Finally, a set of examples and a comparative study were accomplished to test the suitability and validity of the approach. The JADE multi-agent middleware has been used for the practical implementation of the approach.Comment: arXiv admin note: substantial text overlap with arXiv:2402.0949

Agile AI development for Real World Solutions

This keynote will analyse the importance of IoT, Blockchain and Edge Computing as contributors to the development of distributed intelligent systems that have the capacity to interact with the environment "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI, IoT and Blockchain in an Edge Computing model or elsewhere, offers a world of possibilities and opportunities

Distributed intelligent systems for a swarm of robots

Area exploration is a task where a robot tries to gain information about an unknown environment. Exploring an unknown area is a challenging task for a group of robots as no pre-made map exists, leading to setting a suitable swarm formation compatible with the area to be explored. Having a suitable swarm formation allows the swarm to preserve the overall exploration time, by distributing sub-tasks for each robot, and collecting relevant data. Current swarm formations such as biologically inspired formations or Probabilistic RoadMap (PRM) tend to have a fixed shape, where robots are positioned in a fixed location point within the swarm, preventing the swarm from adjusting its formation to adapt to the unknown area, thus, are not suitable to explore unknown areas. One needs a more flexible formation, where each robot can change its position within the swarm. Consequently, this research aims to build a distributed robotic swarm formation using fractals. Fractals have the properties of self-similarity, allowing for an equal distribution of the robots, and recursiveness, allowing for a gradual expansion of a swarm formation. Utilising the properties of fractals allow for a robotic swarm to develop a fractal as a swarm formation. Additionally, changing the parameters of each fractal formation, such as a number of branches, will provide the swarm with the flexibility to adjust the fractal formation and to continue exploring an unknown area. In order to determine both advantages and disadvantages of using fractals as a swarm formation, the first step is to classify each selected fractal into either a line or curve-based formation class to distinguish the similarities and differences in each fractal’s behaviour. The second step is to implement the growth rule of each fractal formation using robots to explore an unknown area. The last step is to study the effect of changing the parameters of the of implemented fractal formations toward exploring unknown areas. The research’s outcome shows that using fractals as a swarm formation achieved near the amount of area covered by a traditional exploration method, such as PRM, with 88% less use of robots. Furthermore, fractal formations balances between the number of robots used, and the amount of area covered as each fractal uses only the robots needed to develop specific iterations. The effect of changing the parameters of a fractal formation increases the chance of covering more areas

Model of evaluation of the efficiency of the ship’s diesel generator control system

The main trend in the development of control systems in industry is to transit from centralized to distributed intelligent systems based on network technologies. With the development of microprocessor technology and telecommunications, the opportunity has appeared to place the information processing means near the automation objects. It allows you to create effective control systems with locally distributed equipment – so called distributed control systems. Operation of power plants and many other objects determines the subject area of the control systems for such objects in real time

Cooperating systems: Layered MAS

Distributed intelligent systems can be distinguished by the models that they use. The model developed focuses on layered multiagent system conceived of as a bureaucracy in which a distributed data base serves as a central means of communication. The various generic bureaus of such a system is described and a basic vocabulary for such systems is presented. In presenting the bureaus and vocabularies, special attention is given to the sorts of reasonings that are appropriate. A bureaucratic model has a hierarchy of master system and work group that organizes E agents and B agents. The master system provides the administrative services and support facilities for the work groups