The high cost of acquiring and operating a collection of advanced autonomous mobile robots for distributed robotic system (DRS) experiments stimulates the interest of developing a generic, real-time, virtual reality b...
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The high cost of acquiring and operating a collection of advanced autonomous mobile robots for distributed robotic system (DRS) experiments stimulates the interest of developing a generic, real-time, virtual reality based simulation platform. The specification and design principles for such a system is presented. New concepts introduced in the paper include levels of users for the simulation platform, abstract sensors/actuators; the bandwidth of sensing/actuating devices, the bandwidth of a robot and that for the simulation platform; integrating hard systems with computer simulation; and simulated concurrency. A multiprocessor based DRS simulation platform, currently being developed at the Robotics Laboratory, UC Riverside is used as an example.
An idea of autonomous agents that arises as an extension of the object and process concepts may be applied to distributed and decentralized systems development. In the paper the authors have undertaken an attempt to d...
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An idea of autonomous agents that arises as an extension of the object and process concepts may be applied to distributed and decentralized systems development. In the paper the authors have undertaken an attempt to describe formally the architecture of multi-agent systems (called M-agent architecture) that may be considered as a starting point to develop a decentralized multiagent, system technology such as Agent Oriented Analysis, Design and Programming.
Smart devices have gained increasing importance and are meanwhile also being considered in a residential environment. For the design of such devices, cost efficiency plays an important role, although the actual demand...
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Smart devices have gained increasing importance and are meanwhile also being considered in a residential environment. For the design of such devices, cost efficiency plays an important role, although the actual demand of hard- and software resources is very difficult to determine in advance. This paper analyses the implementation effort needed for an intelligent, networked refrigerator. It gives an overview of the enhanced functionality as well as the overall usage of resources and their distribution among the different tasks. The network selected for the case study was the European Installation Bus.
A distributed computer network management system consisting of cooperating autonomous computing agents allows network management to be more responsive due to information gathering and network recovery activities being...
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A distributed computer network management system consisting of cooperating autonomous computing agents allows network management to be more responsive due to information gathering and network recovery activities being performed in parallel. However, to perform these tasks, the network of agents requires a stable organizational infrastructure. In addition, to meet the needs of human network administrators, the distributed system must allow ultimate authority to be centralized at a single location. distributed Big Brother (DBB) represents a pragmatic blending of diverse technologies from the field of distributed AI, such as contract formation, organizational structuring, election for role assignment, and hierarchical control. The result is an infrastructure for a network management system in which separate agents reconfigure themselves when hardware and software failures occur in order to assure the authority structure demanded by network operators. Our efforts illustrate how integrating existing distributed AI technologies can meet realistic needs, and highlight open problems that require the development of new technologies.
For distributed problem solving systems, there is a need to define knowledge at two levels, one external to the agents and the other internal to the agents. External knowledge is required to achieve cooperation among ...
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For distributed problem solving systems, there is a need to define knowledge at two levels, one external to the agents and the other internal to the agents. External knowledge is required to achieve cooperation among agents and global convergence of the problem solving process, whereas internal knowledge is required to solve the sub-problems assigned to the agents. External knowledge specifies the role of each agent and its relationship with other agents in the system. Internal knowledge specifies knowledge structure and the problem solving process within each agent. DKRL is an object-oriented language for describing distributed blackboard systems. In DKRL a problem solving system is described as a collection of distributed intelligent, autonomous agents (modelled as objects), cooperating to solve the problem. An agent consists of a knowledge base, a behaviour part, a local controller, a monitor, and a communication controller. DKRL has characteristics of a dynamic nature, i.e. the agents can be created dynamically and the relationship among them also changes dynamically. An agent in DKRL’s computational model cannot be activated by more than one message at the same time and uses a virtual synchrony environment for message transmission among agents. This model combines the advantages of remote procedure calls with those of asynchronous message passing. DKRL allows object-oriented programming techniques to be used for system development and facilitates the development by allowing one-to-one mapping between the objects in the knowledge model and the knowledge base of the agent. In this paper, we give an overview of the distributed blackboard paradigm for which DKRL was developed as well as the design considerations. We also propose and formally describe the underlying models of DKRL and explain how concurrency is exploited by DKRL. We conclude with the current status of and preliminary experience with DKRL in using it for the development of a gate assignment problem.
Federated learning (FL) as a novel paradigm in artificialintelligence (AI), ensures enhanced privacy by eliminating data centralization and brings learning directly to the edge of the user's device. Nevertheless,...
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Federated learning (FL) as a novel paradigm in artificialintelligence (AI), ensures enhanced privacy by eliminating data centralization and brings learning directly to the edge of the user's device. Nevertheless, new privacy issues have been raised particularly during training and the exchange of parameters between servers and clients. While several privacy -preserving FL solutions have been developed to mitigate potential breaches in FL architectures, their integration poses its own set of challenges. Incorporating these privacypreserving mechanisms into FL at the edge computing level can increase both communication and computational overheads, which may, in turn, compromise data utility and learning performance metrics. This paper provides a systematic literature review on essential methods and metrics to support the most appropriate trade-offs between FL privacy and other performance -related application requirements such as accuracy, loss, convergence time, utility, communication, and computation overhead. We aim to provide an extensive overview of recent privacy -preserving mechanisms in FL used across various applications, placing a particular focus on quantitative privacy assessment approaches in FL and the necessity of achieving a balance between privacy and the other requirements of real -world FL applications. This review collects, classifies, and discusses relevant papers in a structured manner, emphasizing challenges, open issues, and promising research directions.
Scheduling is the problem of allocating resources to alternate possible uses over designated period of time. Auction mechanisms use prices derived through distributed bidding protocols to detenmne an allocation. A new...
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Scheduling is the problem of allocating resources to alternate possible uses over designated period of time. Auction mechanisms use prices derived through distributed bidding protocols to detenmne an allocation. A new manufacturing scheduling protocol based on multiagent paradigm is proposed in this paper. We define all the manufacturing units, such as machines and jobs, as economic agents, which conduct strong robustness against practical manufacturing conditions. An auction mechanism with bidding protocol corresponding to market structure is proposed. We study the dYnamism of the proposed scheduling protocol, and confmn its validity by several simulation experiments.
This paper presents a co-operative and reactive model and a multiagent architecture for co-operative scheduling in a jobshop context. First we introduce a workshop scheduling model structured according to three decisi...
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This paper presents a co-operative and reactive model and a multiagent architecture for co-operative scheduling in a jobshop context. First we introduce a workshop scheduling model structured according to three decision levels enabling. through co-operation. to absorb disruptions internal to a production cell composed of autonomous machines. Next we present a multi-agent system currently in development. simulating the reactive and co-operative scheduling of a production cell according to the preceding model. Then. agents and different co-operation protocols allowing them to solve an internal perturbation are described. Finally. we conclude on the limits and the perspectives of our research.
A wide area telecommunication network consists of a set of geographically distributed sites that are connected by communication links. An important and frequently occurring problem in such networks is circuit disrupti...
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A wide area telecommunication network consists of a set of geographically distributed sites that are connected by communication links. An important and frequently occurring problem in such networks is circuit disruption. Circuits may be disrupted by line, node or power failures. Such situations may arise after natural disasters such as hurricanes, earthquakes, floods, or snow storms. Emergency situations such as these disrupt circuits and cause changes in network parameters affecting otherwise routine network activities. In such environments, it is important that the network system has a predefined methodology to plan the restoration process. When a circuit is disrupted, it is desirable to find an optimal or near-optimal alternative path that restores the *** objective of this paper is to apply a distributed artificial intelligence (DAI) methodology to the network restoral planning problem. This approach uses a bidirectional search methodology to identify a near-optimal restoral circuit. The goal is to use dynamic information about a network and domain-specific heuristics to evaluate alternate restoration plans and recommend the best (least-cost) plan under the circumstances.
Abstract Multiagent systems provide a powerful framework for developing real-time process supervision and control systems for distributed and networked processes by automating adaptability and situation-dependent rear...
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Abstract Multiagent systems provide a powerful framework for developing real-time process supervision and control systems for distributed and networked processes by automating adaptability and situation-dependent rearrangement of confidence to specific monitoring and diagnosis techniques. An agent-based framework for monitoring, analysis, diagnosis, and control with agent-based systems (MADCABS) is developed and tested by using detailed models of chemical reactor networks. MADCABS is composed of three main hierarchical layers, the physical communication layer, the supervision layer and the agent management layer. The supervision layer consists of agents and methods for data preprocessing, process monitoring, fault diagnosis, and control. The agent management layer conducts the assessment of agent performances to assign the priorities for selecting the most useful methods of process supervision for specific types of situations. The paper illustrates the operation of MADCABS for monitoring and fault detection.
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