Zeus: A toolkit for building distributed multiagent systems

The multiagent systems approach of knowledge- level cooperation between autonomous agents promises significant benefits to distributed systems engineering, such as enhanced interoperability, scalability, and reconfigurability. However, thus far, because of the innate difficulty of constructing multiagent systems, this promise has been largely unrealized. Hence there is an emerging desire among agent developers to move away from developing point solutions to point problems in favor of developing methodologies and toolkits for building distributed multiagent systems. This philosophy led to the development of the ZEUS Agent Building Toolkit, which facilitates the rapid development of collaborative agent applications through the provision of a library of agent- level components and an environment to support the agent-building process. The ZEUS toolkit is a synthesis of established agent technologies with some novel solutions to provide an integrated collaborative agent-building environment.     

Visualization and debugging of distributed multiagent systems

Visualizing the behavior of systems with distributed data, control, and process is a notoriously difficult task. Each component in the distributed system has only a local view of the whole setup, and the onus is on the user to integrate, into a coherent whole, the large amounts of limited information they provide. In this article, we describe an architecture and an implemented system for visualizing and controlling distributed multiagent applications. The system comprises a suite of tools, with each tool providing a different perspective of the application being visualized . Each tool interrogates the components of the distributed application, collates the returned information, and presents this information to users in an appropriate manner. This in essence, shifts the burden ofinference from the user to the visualizer. Our visualizer has been evaluated on four distributed multiagent systems: a travel management application, a telecommunications network management application, a business process management demonstrator, and an electronic commerce application. Lastly, we briefly show how the suite of tools can be used together for debugging multiagent applications - a process we refer to as debugging via corroboration.     

Infrastructures and tools for multiagent systems for the new generation of distributed systems

In order for multiagent systems to be included in real domains (media and Internet, logistics, e-commerce, and health care), infrastructures and tools for multiagent systems should provide efficiency, scalability, security, management, monitoring, and other features related to building real applications. Thus, infrastructures and tools that support multiagent systems are needed, especially those that promote the adoption of agent-based systems by designers and programmers in both academia and industry. This special issue is a selection of contributions whose preliminary versions were presented at the ITMAS 2010 workshop, which was held in conjunction with the International Conference on Autonomous Agents and Multi-agent Systems.     

A framework for simulating real-time multi-agent systems

In this paper, we describe an implementation of use in demonstrating the effectiveness of architectures for real-time multi-agent systems. The implementation provides a simulation of a simplified RoboCup Search and Rescue environment, with unexpected events, and includes a simulator for both a real-time operating system and a CPU. We present experimental evidence to demonstrate the benefit of the implementation in the context of a particular hybrid architecture for multi-agent systems that allows certain agents to remain fully autonomous, while others are fully controlled by a coordinating agent. In addition, we discuss the value of the implementation for testing any models for the construction of real-time multi-agent systems and include a comparison to related work.

Fully distributed consensus for fuzzy multiagent systems with jointly connected topology

This paper investigates the problem of fully distributed consensus for polynomial fuzzy multiagent systems (MASs) under jointly connected topologies. First, a polynomial fuzzy modeling method is presented to characterize the error dynamics that is constructed by one leader and multiple followers. Then, using the relative state information and the agents' dynamics, a distributed adaptive protocol is designed to guarantee that MASs under jointly connected topologies can achieve consensus in a fully distributed fashion. Utilizing the Lyapunov technique, a relaxed sufficient criterion is proposed to ensure consensus for fuzzy MASs under jointly connected topologies. Moreover, the adaptive coupling weights between neighboring agents can converge to certain values. The derived condition is transformed into a sum-of-squares form, which can be solved numerically. We provide an example to illustrate the proposed distributed adaptive consensus technique's validity.     

具有复杂动力学的多智能体系统分布式优化综述

多智能体系统分布式优化由于其高效性、灵活性和可靠性等特点吸引了大量学者的关注,在多机器人协同控制、无线传感器网络、能源系统等领域具有广泛的应用前景.分布式优化的基本目标是利用智能体的个体目标函数梯度、自身及其邻居状态信息设计分布式控制协议,驱动所有智能体的状态或输出到全局目标函数的最优解,系统动力学是影响智能体状态演化的重要因素.鉴于此,在回顾现有连续时间分布式优化算法的基础上,根据系统动力学分类,尽可能全面地评述具有复杂动力学的多智能体系统分布式优化问题的最新研究进展,并对未来发展方向进行展望.     

RAO logic for multiagent framework

In this paper,we deal with how agents reason about knowledge of others in multiagent system.We first present a knowledge representation framework called reasoning about others(RAO) which is designed specifically to represent concepts and rules used in reasoning about knowledge of others.From a class of sentences usually taken by people in daily life to reason about others,a rule called position exchange principle(PEP)is abstracted.PEP is described as an axiom scheme in RAO and regarded as a basic rule for agents to reason about others,and further it has the similar form and role to modus ponens and(K) axion of knowledge logic.The relationship between speech acts and common sense is also discussed which is necessary for RAO.Based on ideas from situation calculus,this relationship is characterized by an axiom schema in RAO.Our theories are also demonstrated by an example.     

An object-oriented tool for simulating distributed real-time control systems

This paper presents an object-oriented software tool, called RTSIM, aimed at simulating real-time embedded controllers. The tool consists of a collection of C++ libraries permitting a separate specification of the functional behaviour of the controller and of the hardware/software architecture to be used for its deployment. In particular, it is possible to provide an accurate modelling of the concurrent architecture of the control tasks and of the run-time support offered by the operating system for the real-time scheduling of the shared resources (CPU, memory buffers and network links). In this way, it is possible to compare different scheduling solutions by evaluating their simulated performance directly in the domain of the control application. Moreover, the tool can be utilized to tune up design parameters such as the activation frequencies of the tasks. The application of the tool is shown in a meaningful case study. Copyright © 2002 John Wiley & Sons, Ltd.     

Optimal control of distributed multiagent systems with finite-time group flocking

The flocking of multiple intelligent agents, inspired by the swarm behavior of natural phenomena, has been widely used in the engineering fields such as in unmanned aerial vehicle (UAV) and robots system. However, the performance of the system (such as response time, network throughput, and resource utilization) may be greatly affected while the intelligent agents are engaged in cooperative work. Therefore, it is concerned to accomplish the distributed cooperation while ensuring the optimal performance of the intelligent system. In this paper, we investigated the optimal control problem of distributed multiagent systems (MASs) with finite-time group flocking movement. Specifically, we propose two optimal group flocking algorithms of MASs with single-integrator model and double-integrator model. Then, we study the group consensus of distributed MASs by using modern control theory and finite-time convergence theory, where the proposed optimal control algorithms can drive MASs to achieve the group convergence in finite-time while minimizing the performance index of the intelligence system. Finally, experimental simulation shows that MASs can keep the minimum energy function under the effect of optimal control algorithm, while the intelligent agents can follow the optimal trajectory to achieve group flocking in finite time.     

An architectural application framework for evolving distributed systems

In this paper, we present an object-oriented application framework to support the development of evolving distributed systems. The framework combines concepts of the object-oriented paradigm with those of architectural design. The result is a framework which supports extensible and reusable configurations of distributed components. The framework addresses the problem of evolution by allowing the addition, replacement and extension of components of a configuration. Evolution is also possible at run time, where a configuration can change dynamically depending on a particular event. The paper outlines the fundamental aspects of our framework and presents a case study, which illustrates its application.     

Consensus-based evaluation framework for distributed information retrieval systems

Multi-agent systems have been attacking the challenges of information retrieval tasks on distributed environment. In this paper, we propose a consensus choice selection method based framework to evaluate the performance of cooperative information retrieval tasks of the multiple agents. Thereby, two well-known measurements, precision and recall, are extended to handle consensual closeness (i.e., local and global consensus) between the sets of retrieved results. We show that in a motivating example the proposed criteria are prone to solve the rigidity problem of classical precision and recall. More importantly, the retrieved results can be ranked with respect to the consensual score, and the ranking mechanism has been verified to be more reasonable.

PatSearch: an integrated framework for patentability retrieval

Patent retrieval primarily focuses on searching relevant legal documents with respect to a given query. Depending on the purposes of specific retrieval tasks, processes of patent retrieval may differ significantly. Given a patent application, it is challenging to determine its patentability, i.e., to decide whether a similar invention has been published. Therefore, it is more important to retrieve all possible relevant documents rather than only a small subset of patents from the top ranked results. However, patents are often lengthy and rich in technical terms. It is thus often requiring enormous human efforts to compare a given patent application with retrieved results. To this end, we propose an integrated framework, PatSearch, which automatically transforms the patent application into a reasonable and effective search query. The proposed framework first extracts representative yet distinguishable terms from a given application to generate an initial search query and then expands the query by combining content proximity with topic relevance. Further, a list of relevant patent documents will be retrieved based on the generated queries to provide enough information to assist patent analysts in making the patentability decision. Finally, a comparative summary is generated to assist patent analysts in quickly reviewing retrieved results related to the patent application. Extensive quantitative analysis and case studies on real-world patent documents demonstrate the effectiveness of our proposed approach.     

An integrated runtime QoS-aware middleware framework for distributed multimedia applications

Future-generation distributed multimedia applications are expected to be highly scalable to a wide variety of heterogeneous devices, and highly adaptive across wide-area distributed environments. This demands multiple stages of run-time support in QoS-aware middleware architectures, particularly, probing the performance of QoS parameters, instantiating the initial component configurations, and adapting to on-the-fly variations. However, few of the past experiences in related work have shown comprehensive run-time support in all of the above stages – they often design and build a middleware framework by focusing on only one of the run-time issues. In this paper, we argue that distributed multimedia applications need effective run-time middleware support in all these stages to be highly scalable and adaptive across a wide variety of execution environments. Nevertheless, the design of such a middleware framework should be kept as streamlined and simple as possible, leading to a novel and integrated run-time middleware platform to unify the probing, instantiation and adaptation stages. In addition, for each stage, the framework should enable the interaction of peer middleware components across host boundaries, so that the corresponding middleware function can be performed in a coordinated and coherent fashion. We present the design of such an integrated architecture, with a case study to illustrate how it is simple yet effective to monitor and configure complex multimedia applications.     

On load balancing for distributed multiagent computing

Multiagent computing on a cluster of workstations is widely envisioned to be a powerful paradigm for building useful distributed applications. The agents of the system span across all the machines of a cluster. Just like the case of traditional distributed systems, load balancing becomes an area of concern. With different characteristics between ordinary processes and agents, it is both interesting and useful to investigate whether conventional load-balancing strategies are also applicable and sufficient to cope with the newly emerging needs, such as coping with temporally continuous agents, devising a performance metric for multiagent systems, and taking into account the vast amount of communication and interaction among agent. This paper discusses the above issues with reference to agent properties and load balancing techniques and outlines the space of load-balancing design choices in the arena of multiagent computing. In view of the special agent characteristics, a novel communication-based load-balancing algorithm is proposed, implemented, and evaluated. The proposed algorithm works by associating a credit value with each agent. The credit of an agent depends on its affinity to a machine, its current workload, its communication behavior, and mobility, etc. When a load imbalance occurs, the credits of all agents are examined and an agent with a lower credit value is migrated to relatively lightly loaded machine in the system. Quasi-simulated experiments of this algorithm show load-balancing improvement compared with conventional workload-oriented load-balancing schemes.     

基于自适应分布式滤波观测器的多智能体系统编队控制

本文考虑了全局指令系统输出信息受到信道扰动情况下线性多智能体系统的编队控制问题.首先,基于协作式输出调节理论框架对线性多智能体系统的编队控制问题进行数学建模.其次,针对受到信道扰动的全局指令系统输出信息,提出了一类基于受扰输出的自适应分布式滤波观测器,在降低网络信息交换量的同时消除扰动的影响.最后,设计了输出反馈确定等价控制律,解决了线性多智能体系统的分布式编队控制问题.给出了数值仿真结果检验控制性能.     

An agent-based distributed computational experiment framework for virtual supply chain network development

This paper focuses on research on virtual supply chain networks instead of real supply chain networks by making use of agent technology and computational experiment method. However, the recent research is inefficient in computational experiment modeling and lack of a related methodological framework. This paper proposes an agent-based distributed computational experiment framework with in-depth study of material flow, information flow and time flow modeling in supply chain networks. In this framework, a matrix-based formal representation method for material flow, a task-centered representation method for information flow and an agent-based time synchronization mechanism for time flow are proposed to aid building a high quality computational experiment model for a multi-layer supply chain network. In order to conduct the model, a computational experiment architecture for virtual supply chain networks is proposed. In this architecture, coordination mechanisms among agents based on material flow, information flow and time flow as well as consistency check methods for computational experiment models are discussed. Finally, an implementation architecture of the framework is given and a case of virtual supply chain network is developed to illustrate the application of the framework. The computational experiment results of the case show that the proposed framework, not only feasible but correct, has sound advantages in virtual supply chain network development, computational experiment modeling and implementation.     

An integrated modelling framework for regulated river systems

Management of regulated water systems has become increasingly complex due to rapid socio-economic growth and environmental changes in river basins over recent decades. This paper introduces the Source Integrated Modelling System (IMS), and describes the individual modelling components and how they are integrated within it. It also describes the methods employed for tracking and assessment of uncertainties, as well as presenting outcomes of two case study applications.Traditionally, the mathematical tools for water resources planning and management were generally designed for sectoral applications with, for example, groundwater being modelled separately from surface water. With the increasing complexity of water resources management in the 21st century those tools are becoming outmoded. Water management organisations are increasingly looking for new generation tools that allow integration across domains to assist their decision making processes for short-term operations and long-term planning; not only to meet current needs, but those of the future as well.In response to the need for an integrated tool in the water industry in Australia, the eWater Cooperative Research Centre (CRC) has developed a new generation software package called the Source IMS. The Source IMS is an integrated modelling environment containing algorithms and approaches that allow defensible predictions of water flow and constituents from catchment sources to river outlets at the sea. It is designed and developed to provide a transparent, robust and repeatable approach to underpin a wide range of water planning and management purposes. It can be used to develop water sharing plans and underpin daily river operations, as well as be used for assessments on water quantity and quality due to changes in: i) land-use and climate; ii) demands (irrigation, urban, ecological); iii) infrastructure, such as weirs and reservoirs; iv) management rules that might be associated with these; and v) the impacts of all of the above on various ecological indices. The Source IMS integrates the existing knowledge and modelling capabilities used by different state and federal water agencies across Australia and has additional functionality required for the river system models that will underpin the next round of water sharing plans in the country. It is built in a flexible modelling environment to allow stakeholders to incorporate new scientific knowledge and modelling methods as they evolve, and is designed as a generic tool suitable for use across different jurisdictions. Due to its structure, the platform can be extended/customised for use in other countries and basins, particularly where there are boundary issues.     

An integrated framework for card-based production control systems

Although card-based control systems, such as Kanban and CONWIP, for production processes have been successfully employed, the design discipline does not seem to be clear yet. Therefore, the superiority of one control over the other is controversial. This paper proposes a novel design discipline for card-based control of production processes, by developing the theory of token transaction systems. The theory shows how the three indices represented in Little??s law are decided by the structure of a production process with control cards and deployment of work-in-process (WIP). That is, the relation of WIP, cycle time and throughput on specific sub-network of a production process is clarified. We show how Little??s law should be used in the design of card-based production control systems. As an application of the theory, we resolve complicated result of comparison between Kanban and CONWIP. In doing so, this theory does not restrict the target of analysis to serial production lines, but any shaped processes can be analyzed.     

A multiagent framework for automated online bargaining

Automated online bargaining is currently limited by fixed price-issuing capabilities. The authors propose a multi-agent framework for automated bargaining that uses pattern matching and a dynamic price-issuing agent to better serve customers and the seller's bottom line.     

A computational model for distributed knowledge systems with learning mechanisms

This paper addresses the issues of machine learning in distributed knowledge systems, which will consist of distributed software agents with problem solving, communication and learning functions. To develop such systems, we must analyze the roles of problem-solving and communication capabilities among knowledge systems. To facilitate the analyses, we propose a computational model: LPC. The model consists of a set of agents with (a) a knowledge base for learned concepts, (b) a knowledge base for problem solving, (c) prolog-based inference mechanisms and (d) a set of beliefs on the reliability of the other agents. Each agent can improve its own problem-solving capabilities by deductive learning from the given problems, by memory-based learning from communications between the agents and by reinforcement learning from the reliability of communications between the other agents. An experimental system of the model has been implemented in Prolog language on a Window-based personal computer. Intensive experiments have been carried out to examine the feasibility of the machine learning mechanisms of agents for problem-solving and communication capabilities. The experimental results have shown that the multiagent system improves the performance of the whole system in problem solving, when each agent has a higher learning ability or when an agent with a very high ability for problem solving joins the organization to cooperate with the other agents in problem solving. These results suggest that the proposed model is useful in analyzing the learning mechanisms applicable to distributed knowledge systems.