Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

Model checking temporal knowledge and commitments in multi-agent systems using reduction

Though modeling and verifying Multi-Agent Systems (MASs) have long been under study, there are still challenges when many different aspects need to be considered simultaneously. In fact, various frameworks have been carried out for modeling and verifying MASs with respect to knowledge and social commitments independently. However, considering them under the same framework still needs further investigation, particularly from the verification perspective. In this article, we present a new technique for model checking the logic of knowledge and commitments (CTLKC⁺). The proposed technique is fully-automatic and reduction-based in which we transform the problem of model checking CTLKC⁺ into the problem of model checking an existing logic of action called ARCTL. Concretely, we construct a set of transformation rules to formally reduce the CTLKC⁺ model into an ARCTL model and CTLKC⁺ formulae into ARCTL formulae to get benefit from the extended version of NuSMV symbolic model checker of ARCTL. Compared to a recent approach that reduces the problem of model checking CTLKC⁺ to another logic of action called GCTL¹, our technique has better scalability and efficiency. We also analyze the complexity of the proposed model checking technique. The results of this analysis reveal that the complexity of our reduction-based procedure is PSPACE-complete for local concurrent programs with respect to the size of these programs and the length of the formula being checked. From the time perspective, we prove that the complexity of the proposed approach is P-complete with regard to the size of the model and length of the formula, which makes it efficient. Finally, we implement our model checking approach on top of extended NuSMV and report verification results for the verification of the NetBill protocol, taken from business domain, against some desirable properties. The obtained results show the effectiveness of our model checking approach when the system scales up.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

An immune oriented multi-agent system for biological image processing

In this article, we present a parallel image processing system based on the concept of reactive agents. Our system lies in the oRis language, which allows to describe finely and simply the agents’ behaviors to detect image features. We also present a method of segmentation using a multi-agent system, and two biological applications made with oRis. The stopping of this multi-agent system is implemented through a technique issued from immunology: the apoptosis.     

Cooperative behavior of various agents in dynamic environment

The multi-agent model is a model in which agents with limited ability cooperate each other to accomplish a goal. In this paper, we introduce a multi-agent model in which agents are created to imitate real ants. There are two different type of agents, each type of which has a particular task. We designed agents to communicate each other by using pheromone considering noise. On this model, we observed cooperative behavior of agents and evaluated conditions to make an order of behavior in the model.     

Collaborating freight forwarding enterprises

The paper presents a model for the collaboration among independent freight forwarding entities. In the modern highly competitive transportation branch freight forwarders reduce their fulfillment costs by exploiting different execution modes (self-fulfillment and subcontraction). For self-fulfillment they use their own vehicles to execute the requests and for subcontracting they forward the orders to external freight carriers. Further enhancement of competitiveness can be achieved if the freight forwarders cooperate in coalitions in order to balance their request portfolios. Participation in such a coalition gains additional profit for the entire coalition and for each participant, therefore reinforcing the market position of the partners. The integrated operational transport problem as well as existing collaboration approaches are introduced. The presented model for collaboration is based on theoretical foundations in the field of combinatorial auctions and operational research game theory. It is applicable for coalitions of freight forwarders, especially for the collaboration of Profit Centres within large freight forwarding companies. The proposed theoretical approach and the presented collaboration model are suitable for a coalition of freight forwarding companies with nearly similar potential on the market.     

基于多Agent的协同制造平台的研究与设计

随着企业间的集成与协作越来越紧密,知识也越来越高度密集,本文基于多Agent技术,提出一种支持以知识为基础的协同制造开发平台——基于多Agent的可伸缩的多智能协同制造平台。详细设计了多Agent协同开发平台的体系结构、协作框架,以及多agent系统基于本体的通信机制。该平台凭借包装服务和代理商提供的通信能力,通过有效地支持制造业知识的表示、获取、检索和重利用,不仅有利于多学科的个体和团队的组织和协作,而且提出的基于本体库的广义知识库能使设计人员有效地、不受限制地交换信息和知识。     

MicroPort: A general simulation platform for seaport container terminals

Seaport container terminals are essential nodes in sea cargo transportation networks. As such, the operational efficiency of container terminals in handling containers passing through them plays a critical role in a globalized world economy. Many models and algorithms have been developed to address various decision problems in container terminals to help improve operational efficiency. These decision support tools are usually used separately for specific purposes. However, the problems they are trying to tackle are often interrelated. Therefore, in this regard, an evaluation tool which can capture as many operational conditions as possible for different decision problems is necessary. This paper introduces a general simulation platform, named MicroPort, which aims to provide an integrated and flexible modeling system for evaluating the operational capability and efficiency of different designs of seaport container terminals. The software structure of MicroPort comprises three programming layers: (1) the Functions layer; (2) the Applications layer; and (3) the Extensions layer. Different layers are bound by Application Programming Interfaces (APIs). Basic functions built in the Functions layer support the Applications layer in which major operation processes can be modeled by an agent-based method. External modules and decision support tools in the Extensions layer then use APIs to adjust the system to produce suitable simulation models for specific purposes.     

线性多智能体系统一致性问题的部分稳定性方法（英文）

A linear transformation is proposed to deal with the consensus problem of high-order linear multi-agent systems(LMASs).In virtue of the linear transformation, the consensus problem is equivalently translated into a partial stability problem. We discuss three issues of the LMASs under a generalized linear protocol: 1) to find criteria of consensus convergence; 2) to calculate consensus function; 3) to design gain matrices in the linear consensus protocol. Precisely, we provide a necessary and sufficient criterion of consensus convergence in terms of Hurwitz stability of a matrix and give an analytical expression of the consensus function. In addition, we set up a relation between the gain matrices in the protocol and the convergence time and consensus accuracy of the agents, and then design the gain matrices with respect to a pre-specified convergence time and a required consensus accuracy.