Consensus recovery of multi-agent systems subjected to failures

Consensus recovery is one of the most important topics of multi-agent systems subjected to failures, but there are few literatures considering this theme. In this article, two disastrous failures are introduced and corresponding recovery programs are proposed to investigate the consensus recovery of multi-agent systems. The failures of multi-agent systems mainly refer to the failures to the agents and links. The cut-agent and cut-link are presented to better understand the disastrous failures of multi-agent systems, and they are the most important components of multi-agent systems. When the cut-agent and cut-link are removed from the systems, the multi-agent systems will become disconnected, and the neighbour agents of cut-agent are used to recover the consensus of multi-agent systems in this article. For different failures, the different measures are taken to restore the connectivity of multi-agent systems, so as to recover the consensus of multi-agent systems. Under the proposed recovery strategies, the convergence speed of achieving consensus of the resulting networks is considered as the main target. Moreover, there is a trade-off between the convergence speed and robustness of the resulting networks.     

Command-filter-based distributed containment control of nonlinear multi-agent systems with actuator failures

In this paper, the problem of distributed containment fault-tolerant control for a class of nonlinear multi-agent systems in strict-feedback form is studied. The considered nonlinear multi-agent systems are subject to unknown nonlinear functions and actuator faults with loss of effectiveness and lock-in-place. By resorting to the universal approximation capability of fuzzy logical systems, the command filtered backstepping technique and nonlinear fault-tolerant control theory, distributed controllers are designed recursively. From the Lyapunov stability theory, it is proved that all signals of the resulting closed-loop systems are cooperatively semi-globally uniformly ultimately bounded and the containment errors converge to a small neighbourhood of origin by properly tuning the design parameters. Finally, a numerical example is provided to show the effectiveness of the proposed control method.     

具有执行器故障的不确定多智能体系统自适应动态面控制

本文研究了一类具有输入量化、未建模动态和执行器故障的非线性多智能体系统的一致跟踪问题.引入一个可量测的动态信号消除未建模动态对系统的影响.利用Young’s不等式和高斯函数的性质,有效地处理了多智能体邻居节点在设计的第一步中对子系统的耦合作用.通过将滞回量化器表示为具有有界系数和有界扰动的输入线性函数,并利用动态面控制方法,提出一种自适应神经网络动态面控制方案,简化了控制器的设计,保证了闭环系统的所有信号都是半全局一致终结有界的,所有跟随者都能实现期望的一致性.最后,仿真结果验证了所提出的自适应控制策略的有效性.     

Flexible coordinator design for modeling resource sharing in multi-agent systems

One approach to modeling multi-agent systems (MASs) is to employ a method that defines components which describe the local behavior of individual agents, as well as a special component, called a coordinator. The coordinator component coordinates the resource sharing behavior among the agents. The agent models define a set of local plans, and the combination of local plans and a coordinator defines a system’s global plan. Although earlier work has provided the base functionality needed to synthesize inter-agent resource sharing behavior for a global, conflict-free MAS environment, the lack of coordination flexibility limits the modeling capability at both the local plan level and the global plan level. In this paper, we describe a flexible design method that supports a range of coordinator components. The method defines four levels of coordination and an associated four-step coordinator generation process, which allows for the design of coordinators with increasing capabilities for handling complexity associated with resource coordination. Colored Petri net based simulation is used to analyze various properties that derive from different coordinators and synthesis of a reduced coordinator component is discussed for cases that involve homogeneous agents.     

二阶智能体的分布式非光滑资源分配算法

分布式资源分配问题旨在满足局部约束下完成一定量资源分配的同时使全局成本函数最小.首先,针对无向连通网络下二阶积分器型线性智能体系统,结合Karush-Kuhn-Tucker条件,提出一种初始值任意的分布式优化算法,其中,全局等式约束对偶变量实现比例积分控制,局部凸函数不等式约束对偶变量实现自动获取.当全局成本函数为非光滑凸函数时,借助集值LaSalle不变性原理理论证明所提出算法渐近收敛到全局最优解.其次,将所提出算法推广至无向连通网络下参数未知的Euler-Lagrange多智能体系统.当全局成本函数为非光滑凸函数时,借助Barbalat引理理论证明所提出算法渐近收敛到全局最优解.最后,通过数值仿真验证了所提算法的有效性.     

Probabilistic resource allocation in heterogeneous distributed systems with random failures

The problem of finding efficient workload distribution techniques is becoming increasingly important today for heterogeneous distributed systems where the availability of compute nodes may change spontaneously over time. Resource-allocation policies designed for such systems should maximize the performance and, at the same time, be robust against failure and recovery of compute nodes. Such a policy, based on the concepts of the Derman–Lieberman–Ross theorem, is proposed in this work, and is applied to a simulated model of a dedicated system composed of a set of heterogeneous image processing servers. Assuming that each image results in a “reward” if its processing is completed before a certain deadline, the goal for the resource allocation policy is to maximize the expected cumulative reward. An extensive analysis was done to study the performance of the proposed policy and compare it with the performance of some existing policies adapted to this environment. Our experiments conducted for various types of task-machine heterogeneity illustrate the potential of our method for solving resource allocation problems in a broad spectrum of distributed systems that experience high failure rates.     

Diagnosing IC failures in a fast environment

In today's world of fast product introductions, short product life cycles, and complex ICs, manufacturers need comprehensive, accurate, and fast diagnosis of development failures and reliability hazards before launching a product. As a result, the failure analyst's role has changed from reactive to proactive. Waiting until failures come back from the field to assess the reliability of a product is no longer efficient. By that time it's too late. Devising tests that anticipate all failure modes and accelerating those failures within a reasonable length of time is also impractical if not impossible. There are too many variables and too many unknowns in the equation to use this shotgun approach. It results in too many reliability tests that produce no failures, and failure rates that too often are calculated on the basis of potentially irrelevant tests. To adequately evaluate the reliability of a component, the proactive failure analyst must first closely inspect it for both physical and electrical hazards. Combining this data with field data from products using the same technology, the analyst can run realistic tests that stress the part to failure, thus deriving an appropriate failure rate. The proactive failure analyst's role is to detect failures before they occur and assist the circuit designer in eliminating their cause before the product ships. This means that the analyst must conduct evaluation testing with an eye toward the physics of component failure. The result is improved manufacturing yield and a more robust, more reliable component in the field     

Searching for clues: Diagnosing IC failures

Reviewed in this issue Data Mining and Diagnosing IC Fails, by Leendert M. Huisman (Springer, 2005, ISBN 0-387-24993-1, 250 pp., $129). As ICs grow in size, finding a failure's cause has become more and more difficult. This book describes how statistical methods can help uncover the source of a problem using data provided by IC test. It discusses failures during IC manufacture, rather than at the board or assembly level or in the field. Even though the title does not indicate it, much of this book is devoted to yield enhancement, which makes it far more interesting than if it were completely devoted to traditional diagnosis.     

Proactive resource allocation for asynchronous real-time distributed systems in the presence of processor failures

We present two proactive resource allocation algorithms, RBA^*-FT and OBA-FT, for fault-tolerant asynchronous real-time distributed systems. The algorithms consider an application model where task timeliness is specified by Jensen's benefit functions and the anticipated application workload during future time intervals is described by adaptation functions. In addition, we assume that reliability functions of processors are available a priori. Given these models, our objective is to maximize aggregate task benefit and minimize aggregate missed deadline ratio in the presence of processor failures. Since determining the optimal solution is computationally intractable, the algorithms heuristically compute sub-optimal resource allocations, but in polynomial time. Experimental results reveal that RBA^*-FT and OBA-FT outperform their non-fault-tolerant counterparts in the presence of processor failures. Furthermore, RBA^*-FT performs better than OBA-FT, although OBA-FT incurs better worst-case and amortized computational costs. Finally, we observe that both algorithms robustly withstand errors in the estimation of anticipated failures.     

Diagnosing multiple intermittent failures using maximum likelihood estimation

In fault diagnosis intermittent failure models are an important tool to adequately deal with realistic failure behavior. Current model-based diagnosis approaches account for the fact that a component cj may fail intermittently by introducing a parameter gj that expresses the probability the component exhibits correct behavior. This component parameter gj, in conjunction with a priori fault probability, is used in a Bayesian framework to compute the posterior fault candidate probabilities. Usually, information on gj is not known a priori. While proper estimation of gj can be critical to diagnostic accuracy, at present, only approximations have been proposed. We present a novel framework, coined Barinel, that computes estimations of the gj as integral part of the posterior candidate probability computation using a maximum likelihood estimation approach. Barinel's diagnostic performance is evaluated for both synthetic systems, the Siemens software diagnosis benchmark, as well as for real-world programs. Our results show that our approach is superior to reasoning approaches based on classical persistent failure models, as well as previously proposed intermittent failure models.     

Spectral clustering in multi-agent systems

We examine the application of spectral clustering for breaking up the behavior of a multi-agent system in space and time into smaller, independent elements. We propose clustering observations of individual entities in order to identify significant changes in the parameter space (like spatial position) and detect temporal alterations of behavior within the same framework. Available knowledge of important interactions (events) between entities is also considered. We describe a novel algorithm utilizing iterative subdivisions where clusters are pre-processed at each step to counter spatial scaling, rotation, replay speed, and varying sampling frequency. A method is presented to balance spatial and temporal segmentation based on the expected group size, and a validity measure is introduced to determine the optimal number of clusters. We demonstrate our results by analyzing the outcomes of computer games and compare our algorithm to K-means and traditional spectral clustering.     

Distributed adaptive neural network control for a class of heterogeneous nonlinear multi-agent systems subject to actuation failures

In this paper, the cooperative adaptive consensus tracking problem for heterogeneous nonlinear multi-agent systems on directed graph is addressed. Each follower is modelled as a general nonlinear system with the unknown and nonidentical nonlinear dynamics, disturbances and actuator failures. Cooperative fault tolerant neural network tracking controllers with online adaptive learning features are proposed to guarantee that all agents synchronise to the trajectory of one leader with bounded adjustable synchronisation errors. With the help of linear quadratic regulator-based optimal design, a graph-dependent Lyapunov proof provides error bounds that depend on the graph topology, one virtual matrix and some design parameters. Of particular interest is that if the control gain is selected appropriately, the proposed control scheme can be implemented in a unified framework no matter whether there are faults or not. Furthermore, the fault detection and isolation are not needed to implement. Finally, a simulation is given to verify the effectiveness of the proposed method.     

Supporting Internet-scale multi-agent systems

The Internet provides a large-scale environment for (intelligent) software agents. Agents are autonomous (mobile) processes, capable of communication with other agents, interaction with the world, and adaptation to changes in their environment. Current approaches to support agents are not geared for large-scale settings. The near future holds thousands of agents, hosts, messages, and migratory movements of agents. These large-scale aspects require a new approach to facilitate the development of agent applications and support. AgentScape is a scalable agent-based distributed system, described in this paper, that aims at tackling these aspects.     

Structural controllability of multi-agent networks: Robustness against simultaneous failures

In this paper, structural controllability of a leader–follower multi-agent system with multiple leaders is studied from a graph-theoretic point of view. The problem of preservation of structural controllability under simultaneous failures in both the communication links and the agents is investigated. The effects of the loss of agents and communication links on the controllability of an information flow graph are previously studied. In this work, the corresponding results are exploited to introduce some useful indices and importance measures that help characterize and quantify the role of individual links and agents in the controllability of the overall network. Existing results are then extended by considering the effects of losses in both links and agents at the same time. To this end, the concepts of joint (r,s)$(r,s)$-controllability and joint t$t$-controllability are introduced as quantitative measures of reliability for a multi-agent system, and their important properties are investigated. Lastly, the class of jointly critical digraphs is introduced, and it is stated that if a digraph is jointly critical, then joint t$t$-controllability is a necessary and sufficient condition for remaining controllable following the failure of any set of links and agents, with cardinality less than t$t$. Various examples are exploited throughout the paper to elaborate on the analytical findings.     

Dynamic adaptive autonomy in multi-agent systems

Multi-agent systems require adaptability to perform effectively in complex and dynamic environments. This article shows that agents should be able to benefit from dynamically adapting their decision-making frameworks. A decision-making framework describes the set of multi-agent decision-making interactions exercised by members of an agent group in the course of pursuing a goal or set of goals. The decision-making interaction style an agent adopts with respect to other agents influences that agent's degree of autonomy. The article introduces the capability of Dynamic Adaptive Autonomy (DAA), which allows an agent to dynamically modify its autonomy along a defined spectrum (from command-driven to consensus to locally autonomous/master) for each goal it pursues. This article presents one motivation for DAA through experiments showing that the ‘best’ decision-making framework for a group of agents depends not only on the problem domain and pre-defined characteristics of the system, but also on run-time factors that can change during system operation. This result holds regardless of which performance metric is used to define ‘best’. Thus, it is possible for agents to benefit by dynamically adapting their decision-making frameworks to their situation during system operation.     

Normative conflict resolution in multi-agent systems

Norms (permissions, obligations and prohibitions) offer a useful and powerful abstraction with which to capture social constraints in multi-agent systems. Norms should exclude disruptive or antisocial behaviour without prescribing the design of individual agents or restricting their autonomy. An important challenge, however, in the design and management of systems governed by norms is that norms may, at times, conflict with one another; e.g, an action may be simultaneously prohibited and obliged for a particular agent. In such circumstances, agents no longer have the option of complying with these norms; whatever they do or refrain from doing will lead to a social constraint being broken. In this paper, we present mechanisms for the detection and resolution of normative conflicts. These mechanisms, based on first-order unification and constraint solving techniques, are the building blocks of more sophisticated algorithms we present for the management of normative positions, that is, the adoption and removal of permissions, obligations and prohibitions in societies of agents. We capture both direct and indirect conflicts between norms, formalise a practical concept of authority, and model conflicts that may arise as a result of delegation. We are able to formally define classic ways for resolving conflicts such as lex superior and lex posterior.     

Escape and intervention in multi-agent systems

This paper describes the escape/intervention concept as it is used in the agent growing environment framework. The Escape and Intervention is used in many multi-disciplinary areas, including agent research, artificial intelligence, groupware and workflow, process support, software engineering, and social sciences. Based on an ontological perspective, this paper explains how an interaction-oriented agent architecture and language (used for modelling, simulation, and development) makes use of an interaction pattern that is inspired from social contexts seen as multi-agent systems.     

Revealing bullying patterns in multi-agent systems

Multi-agent systems communication is arguably difficult to be designed. An equal balance of communication among agents is usually desirable. When this does not happen, it affects the quality of service, causing poor performances in terms of response times. The aim of this work is to detect undesirable patterns of communication in these types of systems, using the metaphor of bullying. Though observed in social environments, it can also be applied to a multi-agent system scenario: it consists of some agents’ continuous communications toward the same agents. This causes an unbalanced communication, overloading the receiver, which results in higher response times. Some metrics have been designed to measure the agents’ communication, and some rules classify agents accordingly into several patterns regarding the bullying metaphor. Furthermore, the experimental results prove that the metrics based on the bullying metaphor are strongly related with the quality of service of multi-agent systems. The experimental results also show that the causes of a bad quality of service can be figured out by means of this bullying metaphor and their metrics and classification rules.     

多主体系统中的模糊匹配

分析了目前主体服务匹配中存在的问题和不足，针对多主体系统中模糊匹配的需求，提出了一种主体模糊服务描述方法．在模糊服务描述的基础上提出了4种模糊匹配算法：基于语法的近似模糊匹配算法、基于语义的近似模糊匹配算法、等价模糊匹配算法和插入模糊匹配算法，并给出了它们的实现方法．这些算法克服了目前主体服务匹配算法中存在的不足．