Robust event‐triggered control of second‐order disturbed leader‐follower MASs: A nonsingular finite‐time consensus approach

This paper addresses the finite‐time and the prescribed finite‐time event‐triggered consensus tracking problems for second‐order multi‐agent systems (MASs) with uncertain disturbances. The prescribed finite‐time event‐triggered consensus of the second‐order disturbed MASs was obtained for the first time and the controller is nonsingular. Furthermore, a new self‐triggered control scheme is presented for the finite‐time consensus tracking, and the continuous communication can be avoided in the triggering condition monitoring. Hence, the finite‐time consensus tracking can be achieved with intermittent communication. Moreover, Zeno behavior is excluded for each follower. The efficiency of the proposed algorithms is verified by numerical simulations.     

Agents in object‐oriented software engineering

Software engineers of multi‐agent systems (MASs) are faced with different concerns such as autonomy, adaptation, interaction, collaboration, learning, and mobility, which are essentially different from classical concerns addressed in object‐oriented software engineering. MAS developers, however, have relied mostly on object‐oriented design techniques and programming languages, such as Java. This often leads to a poor separation of MAS concerns and in turn to the production of MASs that are difficult to maintain and reuse. This paper discusses software engineering approaches for MASs, and presents a new method for integrating agents into object‐oriented software engineering from an early stage of design. The proposed approach encourages the separate handling of MAS concerns, and provides a disciplined scheme for their composition. Our proposal explores the benefits of aspect‐oriented software development for the incorporation of agents into object‐oriented systems. We also illustrate our aspect‐oriented approach through the Portalware multi‐agent system, a Web‐based environment for the development of e‐commerce portals. Copyright © 2004 John Wiley & Sons, Ltd.     

NNs‐Based Event‐Triggered Consensus Control of a Class of Uncertain Nonlinear Multi‐Agent Systems

We are concerned with the consensus problem for a class of uncertain nonlinear multi‐agent systems (MASs) connected through an undirected communication topology via event‐triggered approaches in this paper. Two distributed control strategies, the adaptive centralized event‐triggered control one and adaptive distributed event‐triggered control one, are presented utilizing neural networks (NNs) and event‐driven mechanisms, where the advantages of the proposed control laws lie that they remove the requirement for exact priori knowledge about parameters of individual agents by taking advantage of NNs approximators and they save computing and communication resources since control tasks only execute at certain instants with respect to predefined threshold functions. Also, the trigger coefficient can be regulated adaptively with dependence on state errors to ensure not only the control performance but also the efficiency of the network interactions. It is proven that all signals in the closed‐loop system are bounded and the Zeno behavior is excluded. Finally, simulation examples are presented for illustration of the theoretical claims.     

A Framework for Coordinated Control of Multiagent Systems and Its Applications

In this paper, a framework is proposed for the distributed control and coordination of multiagent systems (MASs). In the proposed framework, the control of MASs is regarded as achieving decentralized control and coordination of agents. Each agent is modeled as a coordinated hybrid agent, which is composed of an intelligent coordination layer and a hybrid control layer. The intelligent coordination layer takes the coordination input, plant input, and workspace input. In the proposed framework, we describe the coordination mechanism in a domain-independent way, i.e., as simple abstract primitives in a coordination rule base for certain dependence relationships between the activities of different agents. The intelligent coordination layer deals with the planning, coordination, decision making, and computation of the agent. The hybrid control layer of the proposed framework takes the output of the intelligent coordination layer and generates discrete and continuous control signals to control the overall process. To verify the feasibility of the proposed framework, experiments for both heterogeneous and homogeneous MASs are implemented. The proposed framework is applied to a multicrane system, a multiple robot system, and a MAS consisting of an overhead crane, a mobile robot, and a robot manipulator. It is demonstrated that the proposed framework can model the three MASs. The agents in these systems are able to cooperate and coordinate to achieve a global goal. In addition, the stability of systems modeled using the proposed framework is also analyzed.     

A hybrid trust model using reinforcement learning and fuzzy logic

Multiagent systems (MASs) are increasingly popular for modeling distributed environments that are highly complex and dynamic, such as e‐commerce, smart buildings, and smart grids. Typically, agents assumed to be goal driven with limited abilities, which restrains them to working with other agents for accomplishing complex tasks. Trust is considered significant in MASs to make interactions effectively, especially when agents cannot assure that potential partners share the same core beliefs about the system or make accurate statements regarding their competencies and abilities. Due to the imprecise and dynamic nature of trust in MASs, we propose a hybrid trust model that uses fuzzy logic and Q‐learning for trust modeling. as an improvement over Q‐learning‐based trust evaluation. Q‐learning is used to estimate trust on the long term, fuzzy inferences are used to aggregate different trust factors, and suspension is used as a short‐term response to dynamic changes. The performance of the proposed model is evaluated using simulation. Simulation results indicate that the proposed model can help agents select trustworthy partners to interact with. It has a better performance compared to some of the popular trust models in the presence of misbehaving interaction partners.     

Consensus of Fractional‐Order Uncertain Multi‐Agent Systems Based on Output Feedback

This paper is devoted to the consensus protocol design for a set of agents with fractional‐order uncertainty dynamics where the fractional order α satisfies 0 < α < 2. For multi‐agent systems (MASs) with fixed undirected topology, a distributed static output feedback protocol is proposed with an undetermined system matrix. Based on model transformation and fractional‐order stability theory, sufficient conditions are derived to ensure the consensus of MASs in terms of linear matrix inequalities (LMIs). Finally, a simulation example is employed to validate the effectiveness of the proposed consensus protocol.     

Toward Self‐Motivated,Cognitive, Continually Planning Agents

We present a flexible initial framework for defining self‐motivated, self‐aware agents in simulated worlds, planning continuously so as to maximize long‐term rewards. While such agents employ reasoned exploration of feasible sequences of actions and corresponding states, they also behave opportunistically and recover from failure, thanks to their continual plan updates and quest for rewards. Our framework allows for both specific and general (quantified) knowledge and for epistemic predicates such as knowing‐that and knowing‐whether. Because realistic agents have only partial knowledge of their world, the reasoning of the proposed agents uses a weakened closed‐world assumption; this has consequences for epistemic reasoning, in particular introspection. The planning operators allow for quantitative, gradual change and side effects such as the passage of time, changes in distances and rewards, and language production, using a uniform procedural attachment method. Question answering (involving introspection) and experimental runs are shown for our particular agent ME in a simple world, demonstrating the value of continual deliberate, reward‐driven planning. Though the primary merit of agents definable in our framework is that they combine all of the aforementioned features, they can also be configured as single or multiple goal‐seeking agents and as such perform comparably with some recent experimental agents.     

A resilient consensus strategy of near‐optimal control for state‐saturated multiagent systems with round‐robin protocol

In this paper, the resilient consensus strategy design problem is investigated for the time‐varying state‐saturated multiagent systems (MASs). A round‐robin protocol is adopted to schedule the communication network among the MASs for the purpose of preventing the data from collision. In presence of the state‐saturation and gain perturbation phenomena, it is literally impossible to obtain the accurate value of the associate cost function, which describes the consensus performance. As an alternative, an upper bound is derived for the cost function to quantify the consensus performance. Then, the resilient consensus strategy is designed such that this upper bound can be minimized in an iterative manner. The sufficient condition is also provided to guarantee that the upper bound of the cost function exists as time goes to infinity. Finally, a numerical example is provided to illustrate the validity of the proposed methodology.     

Tracking Control for Stochastic Multi‐Agent Systems Based on Hybrid Event‐Triggered Mechanism

This paper deals with the leader‐following consensus for nonlinear stochastic multi‐agent systems. To save communication resources, a new centralized/distributed hybrid event‐triggered mechanism (HETM) is proposed for nonlinear multi‐agent systems. HETMs can be regarded as a synthesis of continuous event‐triggered mechanism and time‐driven mechanism, which can effectively avoid Zeno behavior. To model the multi‐agent systems under centralized HETM, the switched system method is applied. By utilizing the property of communication topology, low‐dimensional consensus conditions are obtained. For the distributed hybrid event‐triggered mechanism, due to the asynchronous event‐triggered instants, the time‐varying system method is applied. Meanwhile, the effect of network‐induced time‐delay on the consensus is also considered. To further reduce the computational resources by constantly testing whether the broadcast condition has been violated, self‐triggered implementations of the proposed event‐triggered communication protocols are also derived. A numerical example is given to show the effectiveness of the proposed method.     

Controllability of heterogeneous multiagent systems

The existing results on controllability of multiagent systems (MASs) are mostly based on homogeneous nodes. This paper focuses on controllability of heterogeneous MASs, where the agents are modeled as two types. One type is that the agents have the same high‐order dynamics, and the interconnection topologies of the information flow in different orders are supposed to be different; the other type is that the agents have generic linear dynamics, and the dynamics are supposed to be heterogeneous. For the first type, the necessary and sufficient condition for controllability of heterogeneous‐topology system is derived via combination of Laplacian matrices. For the second type, the contribution also has two parts. The first part supposes that the agents have the same dimensional states and proves that controllability of this kind of MASs is equivalent to the controllability of each node and the whole interconnection topology, while the last parameter of the state feedback vector must not be 0. The second part supposes that the agents may have different dimensional states. For this kind of systems, the concept of β‐controllability is proposed. The necessary and sufficient condition for β‐controllability of heterogeneous‐dynamic systems is also derived and it is also proved that the feedback gain vectors have the effect to improve controllability. Different illustrative examples are provided to demonstrate the effectiveness of the theoretical results in this paper.     

Event‐Based Semiglobal Consensus of Homogenous Linear Multi‐Agent Systems Subject to Input Saturation

In this paper, we consider the semiglobal leader‐following consensus of general linear multi‐agent systems subject to input saturation. First, an event‐triggered control protocol is provided to ensure semiglobal consensus of the multi‐agent systems, in which the agents should continuously monitor the information of their neighbors. Second, a self‐triggered control protocol is proposed to guarantee the semiglobal consensus of the multi‐agent systems, in which the agents only have access to the information of their neighbors in discrete time instants. Moreover, both event‐triggered control protocol and self‐triggered control protocol are proved to be Zeno‐free, that is, the inter‐event times for such two protocols have positive lower bounds. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed event‐based semiglobal consensus protocols.     

AN ACL FOR SPECIFYING FAULT-TOLERANT PROTOCOLS

Agent communication languages (ACLs) should allow the developer to adopt human-like communication mechanisms in agent programming, facilitating the development of distributed protocols in multi-agent systems (MASs). However, to implement robust protocols, ACLs should provide a way to deal with the failures of agents, as MASs are prone to the same failures that can occur in any distributed software system. In this paper, we address this issue showing how an asynchronous ACL that provides high-level mechanisms to deal with crash failures of agents can be effectively used to specify fault tolerant protocols.     

Physical Safety and Cyber Security Analysis of Multi-Agent Systems: A Survey of Recent Advances

Multi-agent systems (MASs) are typically composed of multiple smart entities with independent sensing, communication, computing, and decision-making capabilities. Nowadays, MASs have a wide range of applications in smart grids, smart manufacturing, sensor networks, and intelligent transportation systems. Control of the MASs are often coordinated through information interaction among agents, which is one of the most important factors affecting coordination and cooperation performance. However, unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly, and thus could lead to severe degradation of the whole system performance and even destruction of MASs. This paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks, and our goal is to present a comprehensive survey on recent results on fault estimation, detection, diagnosis and fault-tolerant control of MASs, and cyber attack detection and secure control of MASs subject to two typical cyber attacks. Finally, the paper concludes with some potential future research topics on the security issues of MASs.      

Efficient resource discovery in self‐organized unstructured peer‐to‐peer networks

In unstructured peer‐to‐peer (P2P) networks, two autonomous peer nodes can be connected if users in those nodes are interested in each other's data. Owing to the similarity between P2P networks and social networks, where peer nodes can be regarded as people and connections can be regarded as relationships, social strategies are useful for improving the performance of resource discovery by self‐organizing autonomous peers on unstructured P2P networks. In this paper, we present an efficient social‐like peer‐to‐peer (ESLP) method for resource discovery by mimicking different human behaviours in social networks. ESLP has been simulated in a dynamic environment with a growing number of peer nodes. From the simulation results and analysis, ESLP achieved better performance than current methods. Copyright © 2008 John Wiley & Sons, Ltd.     

Dynamic self‐triggered output‐feedback control for nonlinear stochastic systems with time delays

In this paper, the dynamic self‐triggered output‐feedback control problem is investigated for a class of nonlinear stochastic systems with time delays. To reduce the network resource consumption, the dynamic event‐triggered mechanism is implemented in the sensor‐to‐controller channel. Criteria are first established for the closed‐loop system to be stochastically input‐to‐state stable under the event‐triggered mechanism. Furthermore, sufficient conditions are given under which the closed‐loop system with dynamic event‐triggered mechanism is almost surely stable, and the output‐feedback controller as well as the dynamic event‐triggered mechanism are co‐designed. Moreover, a dynamic self‐triggered mechanism is proposed such that the nonlinear stochastic system with the designed output‐feedback controller is stochastically input‐to‐state stable and the Zeno phenomenon is excluded. Finally, a numerical example is provided to illustrate the effectiveness of proposed dynamic self‐triggered output‐feedback control scheme.     

Debugging complex software systems by means of pathfinder networks

This paper introduces a new methodology based on the use of Pathfinder networks (PFNETs) for the debugging of multi-agent systems (MASs). This methodology is specifically designed to develop a forensic analysis (i.e. a debugging process performed on previously recorded data of the MAS run) of MASs showing complex tissues of relationships between agents (i.e. a high complexity in their social level). Like previous works in the field of forensic analysis of MASs, our approach is performed by considering displays of the system activity which aim to be understandable by human beings. These displays allow us to understand the social behavior of the system, discover emergent behaviors, and debug possible undesirable behaviors. However, it is well known that the visualization of information in a humanly comprehensible way becomes a complex task when large amounts of information have to be represented, as is the case of the social behavior of large-scale MASs. Our methodology tackles this problem through the use of PFNETs, which are considered to reduce the data complexity in order to obtain simple representations that show only the most important global interactions in the system. In addition, the proposed methodology is customizable thanks to the use of two thresholds allowing the user to define the desired specificity level in the display. The proposal is illustrated with a detailed case study considering a complex customer-seller MAS.     

Playing MUSIC — building context‐aware and self‐adaptive mobile applications

Although the idea of context‐awareness was introduced almost two decades ago, few mobile software applications are available today that can sense and adapt to their run‐time environment. The development of context‐aware and self‐adaptive applications is complex and few developers have experience in this area. On the basis of several demonstrators built by the joint European research project MUSIC, this paper describes typical context and adaptation features relevant for the development of context‐aware and self‐adaptive mobile applications. We explain how the demonstrators were realised using the open‐source platform MUSIC and present the feedback of the developers of these demonstrators. The main contribution of this paper is to show how the development complexity of context‐aware and self‐adaptive mobile applications can be mastered by using an adaptation framework such as MUSIC. Copyright © 2012 John Wiley & Sons, Ltd.     

A recursive genetic framework for evolutionary decision-making in problems with high dynamism

Communication and coordination are the main cores for reaching a constructive agreement among multi-agent systems (MASs). Dividing the overall performance of MAS to individual agents may lead to group learning as opposed to individual learning, which is one of the weak points of MASs. This paper proposes a recursive genetic framework for solving problems with high dynamism. In this framework, a combination of genetic algorithm and multi-agent capabilities is utilised to accelerate team learning and accurate credit assignment. The argumentation feature is used to accomplish agent learning and the negotiation features of MASs are used to achieve a credit assignment. The proposed framework is quite general and its recursive hierarchical structure could be extended. We have dedicated one special controlling module for increasing convergence time. Due to the complexity of blackjack, we have applied it as a possible test bed to evaluate the system’s performance. The learning rate of agents is measured as well as their credit assignment. The analysis of the obtained results led us to believe that our robust framework with the proposed negotiation operator is a promising methodology to solve similar problems in other areas with high dynamism.     

多Agent系统安全框架及实现

多agent系统可以被看作是由多个代表其主人(人或组织)的agent组成的社会。在开放的网络环境中,比如Internet,多agent系统面临着许多的安全问题。Extend-Bond系统是开放的、分布的多agent系统。该文对Extend-Bond系统的安全需求进行了分析,根据分析,设计和实现了适用于Extend-Bond系统的安全基础框架。最后给出了关于多agent系统在安全和信任管理上要继续做的工作。     

Towards a goal‐driven approach to action selection in self‐adaptive software

Self‐adaptive software is a closed‐loop system, since it continuously monitors its context (i.e. environment) and/or self (i.e. software entities) in order to adapt itself properly to changes. We believe that representing adaptation goals explicitly and tracing them at run‐time are helpful in decision making for adaptation. While goal‐driven models are used in requirements engineering, they have not been utilized systematically yet for run‐time adaptation. To address this research gap, this article focuses on the deciding process in self‐adaptive software, and proposes the Goal‐Action‐Attribute Model (GAAM). An action selection mechanism, based on cooperative decision making, is also proposed that uses GAAM to select the appropriate adaptation action(s). The emphasis is on building a light‐weight and scalable run‐time model which needs less design and tuning effort comparing with a typical rule‐based approach. The GAAM and action selection mechanism are evaluated using a set of experiments on a simulated multi‐tier enterprise application, and two sample ordinal and cardinal action preference lists. The evaluation is accomplished based on a systematic design of experiment and a detailed statistical analysis in order to investigate several research questions. The findings are promising, considering the obtained results, and other impacts of the approach on engineering self‐adaptive software. Although, one case study is not enough to generalize the findings, and the proposed mechanism does not always outperform a typical rule‐based approach, less effort, scalability, and flexibility of GAAM are remarkable. Copyright © 2011 John Wiley & Sons, Ltd.