Tropism-based cognition: a novel software architecture for agents in colonies

A novel architecture for agents in colonies has been developed in order to investigate certain forms of group interaction. Specifically, we are interested in the extent to which overall goals for a colony can be achieved when each agent is only aware of limited local goals, whether the architecture allows for emergence of unexpected behaviour, and whether explicit communication among agents facilitates or hinders task performance. The architecture supports several forms of learning. Large colonies of agents (as many as 100) have been studied in simulation experiments, where they carried out fetch-and-carry tasks in the presence of predators and with limited energy reserves. In addition, a physical colony of four agents has been fabricated with the same architecture, to ensure that the behaviours observed in simulation were also present in the hardware implementations.     

Detection of undesirable communication patterns in multi-agent systems

Communication design is usually complex in Multi-Agent Systems (MAS) because of dynamic emergent behaviours. The lack of proper quantitative measures to assess alternative solutions and guide an iterative development makes this design even harder. The aim of this work is to efficiently find and describe communication patterns that should be avoided in these systems and identify the agents involved in these patterns. For this purpose, this research presents a suite of novel metrics and classification rules that, respectively, measure agents’ communication and classify their results to describe patterns. This work also provides tools for automatically measuring the metrics and applying the classification rules. In order to evaluate this work, the results of applying these metrics and classification rules have been compared with the quality attribute of performance in several MAS. Performance is measured as the time between a user’s request and the MAS response, and partially represents the factor of the quality of service. The experiments gather four agent-oriented communication designs that belong to two different domains: Crisis-management and Cinema ticket selling. The study reveals that the detected communication patterns are related with performance, and that the proposed metrics can arguably guide the design of communications improving the overall performance of systems.     

Social processes and group decision making: anonymity in group decision support systems

Various social processes in decision-making groups are considered detrimental to the quality of decisions. It is often assumed that removing the ability for groups to exert strong social influence on its members improves group decisions. Group Decision Support Systems (GDSSs) are increasingly used to remedy the social faults of the decision-making process in groups. In these systems, anonymity is seen as a tool to reduce the impact of the group over its members, and therefore as the key to improved group performance. This meta-analytic review examines the assumption that anonymity in GDSSs is beneficial for group decision-making on a range of performance indicators. In 6 meta-analyses of 12 independent investigations there is no support for this hypothesis. The only reliable effect of anonymity was to lead to more contributions, especially more critical ones. An alternative model is presented to account for the findings. This model argues that performance in decision-making groups depends on the social context and relevant social norms as well as on system characteristics such as anonymity. It is concluded that the integration of anonymity into phases of group decision support does not guarantee improved performance.     

Formal verification of multi-agent systems behaviour emerging from cognitive task analysis

The main goal of this paper is to present a novel formal approach to the verification of cognitive task analysis (CTA), an analytic tool that has been successfully used in the design of reactive behaviours, on multi-agent architectures. To achieve this, a formal logical system is developed, whose purpose is to formally check the possible success or failure of the resulting implementation. This logic's focus is on modelling an agent's behaviour based on her goals, perceptions and actions. The article starts by giving a brief introduction to current research in reactive systems and cognitive task analysis. Simple definitions are offered of the basic concepts in these fields: agent, object, reactive behaviour, control, etc. As illustration, the paper offers the results of applying CTA to a simple model of postal delivery. Then, the syntax and semantics of the proposed logic are defined. Finally, the logic is applied to the verification of some of the behaviours resulting of the previous CTA analysis.     

An individualistic approach to social action in multi-agent systems

In this paper, we propose an individualistic formal model of coordination and social action in Multi-Agent Systems (MAS). In the first stage, the agents recognize potential for cooperation by reasoning about how they depend on each other. In the second phase, the agents negotiate directly: Following ‘social’ strategies, the agents propose, accept or refuse different offers until they reach a ‘fair’ agreement. The process itself is seen as an exchange of commissive speech acts, through which the agents adopt social commitments. Finally, joint commitments and societies are defined in individual terms. Joint plans are seen as deals and team activity as a special case of social activity. Moreover, agents are allowed to relax their initial goals and negotiate over compatible subgoals. They will cooperate as long as they have common interests. This approach improves previous models of coordination in three ways: (a) it is simpler, because team and plan formation are considered in the same stage; (b) it is clearer, because there are not representational gaps along the coordination process; (c) it is more comprehensive, because it is applicable to MAS in general, not only to Cooperative Problem Solving (CPS).     

AgentGateway: A communication tool for multi-agent systems

With the rapid growth of multi-agent systems (MAS), there is a pressing need to communicate between different MAS. Various MAS communication standards have been proposed. However, MAS is usually designed to meet special need, thus making it difficult to follow a standard. This paper presents a tool called AgentGateway trying to solve this problem using a simple, effective approach. The tool translates messages from one MAS to intermediate XML-based messages, which then are translated to messages for another MAS. AgentGateway is scalable, meaning that new MAS can easily join it. Furthermore, it provides transparent and reliable communication. A prototype is developed that shows communication between two MAS, namely, Java Agent Template Lite (JATLite) and OAA, can be done using this approach.     

Time-out protocol for task allocation in multi-agent systems

In multi-agent system, the implementation of a time-out protocol can improve workflow coherence and reduce the communication congestion due to the coordination process among agents. This approach is part of effective CIM workflow planning and control for agent-based operations emerging in assembly, manufacturing, service, and logistics. The case study illustrated in this article concerns industrial testing and rework services provided by a shared resource. Results of analyses have been verified by simulation to illustrate how performance improves or deteriorates when different time-out protocols are applied. A general model is introduced for numerical evaluation, identifying the recommended time-out threshold, and the regions where time-out protocols are, or are not useful for better workflow. Measures of workflow coordination performance, including time-oriented and viability-oriented measures are discussed.     

Dynamic non-Bayesian decision making in multi-agent systems

We consider a group of several non-Bayesian agents that can fully coordinate their activities and share their past experience in order to obtain a joint goal in face of uncertainty. The reward obtained by each agent is a function of the environment state but not of the action taken by other agents in the group. The environment state (controlled by Nature) may change arbitrarily, and the reward function is initially unknown. Two basic feedback structures are considered. In one of them — the perfect monitoring case — the agents are able to observe the previous environment state as part of their feedback, while in the other — the imperfect monitoring case — all that is available to the agents are the rewards obtained. Both of these settings refer to partially observable processes, where the current environment state is unknown. Our study refers to the competitive ratio criterion. It is shown that, for the imperfect monitoring case, there exists an efficient stochastic policy that ensures that the competitive ratio is obtained for all agents at almost all stages with an arbitrarily high probability, where efficiency is measured in terms of rate of convergence. It is also shown that if the agents are restricted only to deterministic policies then such a policy does not exist, even in the perfect monitoring case. This revised version was published online in June 2006 with corrections to the Cover Date.     

Extending process automation systems with multi-agent techniques

This paper presents a design of a process automation system extended with multi-agent systems (MAS) and experiments with its implementation. According to this design, MAS can be used to extend the functionality of ordinary process automation systems at higher levels of control. Anticipated benefits of this include enhanced reconfigurability, responsiveness and flexibility of the resulting automation system. The design also takes into account particular characteristics of process automation. An agent platform for process automation is presented as a basis for applying MAS. A FIPA-compliant agent platform is extended with process automation specific functionality. The platform utilizes a hierarchical agent organization and a BDI-agent model. Two applications are implemented using the platform. One of these shows how the techniques of distributed planning can be applied in discrete control. The other provides a model for supervisory continuous control using the techniques of distributed search. Experiments performed with a laboratory test environment using the applications are presented. They are able to demonstrate the feasibility of the approach in test scenarios.     

多智能体系统的自适应群集分布式优化

本文对具有非线性函数群集行为的连续时间多智能体系统的分布式优化问题进行了研究。本文的目的是 使局部代价函数之和最小。每个智能体只知道与其对应的代价函数。为了解决这一问题,本文设计了一个分布式 控制律,在这个研究中该控制律仅仅依赖于自己和邻居的速度。通过李雅普诺夫稳定性证明了多智能体系统的收 敛性,而且在最小化局部代价函数之和的同时所有智能体可以避免碰撞。最后,通过一个仿真案例来说明所获得 的分析结果。     

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.

Distributed adaptive containment control for high-order nonlinear multi-agent systems

This paper considers the adaptive containment control of high-order nonlinear multi-agent systems with nonlinear parameterisation. Without imposing any conditions on the unknown nonlinearities and unknown parameters, the distributed controllers are constructed recursively with only neighbours’ information by using the backstepping design method. Under the assumption that the leaders set is globally reachable, it is shown that all the signals of the closed-loop systems are global uniformly ultimately bounded (UUB), and all the followers will exponentially converge to the convex hull spanned by the dynamic leaders with adjustable tracking errors. Finally, two simulation examples demonstrate the effectiveness of the control scheme.     

基于多Agent系统的分散发电调度规划

根据分散发电、分布式供能的特点提出了混合发电的数学模型,并将分布式多Agent系统的原理应用其中,对多种可再生能源混合发电的方式进行了探讨,提出了一种基于Agent框架下的单体分层、模块式结构.从宏观的角度对混合发电的问题进行了规划设计,赋予了Agent自主性和协调能力,有关联合目标的分解规划算法,在一定程度上克服了自然资源随机变化带来的不确定性.给出的算例说明该方法具有较强的实用性.     

非线性多智能体系统的无模型自适应聚类一致性控制

针对一类模型未知的离散时间非线性多智能体系统聚类一致性问题,提出一种无模型自适应控制算法.首先,假设系统具有固定拓扑,利用伪偏导数概念得到系统的数据关系模型,在考虑多智能体之间耦合系数条件下给出聚类一致性误差,在此基础上设计一种数据驱动的聚类一致性跟踪控制协议;然后,采用压缩映射方法在理论上分析了跟踪误差的收敛性,结果表明所提出算法不需要智能体模型信息即可完成跟踪任务,是一种数据驱动的控制方法;最后,将结果拓展至随机切换拓扑结构的多智能体系统中,数值仿真结果验证了所提出算法的有效性.     

Global adaptive stabilization of infinite-dimensional systems

The purpose of this paper is to show that a first-order adaptive regulator globally stabilizes a class of infinite-dimensional systems having no right half plane zeros. Knowledge of the spectrum of the model's high-frequency gain is not required.     

Dynamic scheduling using a pheromone-based approach in multi-agent systems

The scheduling problems in factory domain applications usually involve many parallel machines, with each machine capable of processing several tasks. In most cases, changing the current machine state to another state to process a different task incurs additional material costs and time. If the overall system can maintain the expected performance, minimizing these state changes is very beneficial, and agent-based approaches inspired by the task allocation strategies of several social insects have gained increasing attention as solutions. The basic concept is based on the stimulus-threshold relation, and an individual agent determines whether it performs a given task or not based on two sets of terms, the environmental external stimuli for the task and the internal threshold values of all possible tasks. In this approach, selecting appropriate threshold values is directly related to the overall system performance, and we present a pheromone-based approach to obtain appropriate threshold values. Each agent maintains a limited, constant-sized task history queue of recently processed tasks, and the information of each agent is individually used to calculate the threshold values of tasks. Based on various experimental results, we show that the performance of the proposed method is comparable to those of other conventional methods.     

网络攻击下多智能体系统动态事件触发一致性

研究网络攻击下一般线性多智能体系统的动态事件触发一致性问题.考虑多智能体系统在受到网络攻击后,被攻击节点的状态会改变,导致与其相应的连接无法工作,设计修复策略恢复被攻击节点及其相应的连接,给出网络攻击下分布式事件触发控制协议.在静态事件触发机制基础上,通过引入动态阈值参数,提出动态事件触发机制.进一步,利用图论、线性矩阵不等式和李雅普诺夫函数方法,给出网络攻击下实现多智能体系统一致性的充分条件,并证明在所提出的动态事件触发条件下,能够有效避免芝诺行为.最后,通过仿真例子来验证理论结果的有效性.     

Dynamic optimization for multi-agent systems with external disturbances

This paper studies the dynamic optimization problem for multi-agent systems in the presence of external disturbances. Different from the existing distributed optimization results, we formulate an optimization problem of continuous-time mufti-agent systems with time-varying disturbance generated by an exosystem. Based on internal model and Lyapunov-based method, a distributed design is proposed to achieve the optimization. Finally, design. an example is given to illustrate the proposed optimization design.     

Observer-based distributed adaptive iterative learning control for linear multi-agent systems

This paper investigates the consensus problem for linear multi-agent systems from the viewpoint of two-dimensional systems when the state information of each agent is not available. Observer-based fully distributed adaptive iterative learning protocol is designed in this paper. A local observer is designed for each agent and it is shown that without using any global information about the communication graph, all agents achieve consensus perfectly for all undirected connected communication graph when the number of iterations tends to infinity. The Lyapunov-like energy function is employed to facilitate the learning protocol design and property analysis. Finally, simulation example is given to illustrate the theoretical analysis.     

Leader-following consensus of fractional-order multi-agent systems via adaptive pinning control

In this paper, the leader-following consensus problem of fractional-order multi-agent systems is considered via adaptive pinning control. The dynamics of leader and all followers with linear and nonlinear functions are investigated, respectively. We assume that the node should be pinned if its in-degree is less than its out-degree in the paper. Under this assumption and based on the stability theory of fractional-order differential systems, some leader-following consensus criteria are derived, which are easily obtained by matrix inequalities. The control of each agent using local information is designed and detailed analysis of the leader-following consensus is presented. The design technique is based on algebraic graph theory and the Riccati inequality. Several simulation examples are presented to demonstrate the effectiveness of the proposed method.