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A multi-agent system for fault location and service restoration in power distribution systems 配电系统故障定位与恢复的多智能体系统
IF 0.7 Pub Date : 2020-02-10 DOI: 10.3233/mgs-190316
Azeroual Mohamed, T. Lamhamdi, Hassan El Moussaoui, Hassane El Markhi
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引用次数: 4
A novel approach for choosing dynamic community detection algorithms using PROMETHEE II 一种基于PROMETHEE II的动态社区检测算法选择新方法
IF 0.7 Pub Date : 2020-02-10 DOI: 10.3233/mgs-190315
S. Arab, N. Taghezout, F. Benkaddour
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引用次数: 0
A new task scheduling mechanism in grid computing using trust model 基于信任模型的网格计算任务调度机制
IF 0.7 Pub Date : 2020-01-01 DOI: 10.3233/MGS-200325
A. Mohsenzadeh, M. Nickray, Amir Jalaly Bidgoly, Y. Farjami
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引用次数: 1
Petri net and rewriting logic based formal analysis of multi-agent based safety-critical systems 基于Petri网和重写逻辑的多智能体安全关键系统形式化分析
IF 0.7 Pub Date : 2020-01-01 DOI: 10.3233/mgs-200320
A. Boucherit, Laura M. Castro, Abdallah Khababa, O. Hasan
The formal design and development of multi-agent systems has attracted a considerable attention over the past decades because of their extensive use in safety-critical applications. This paper presents an efficient, hybrid and scalable formal development approach for safetycritical systems based on the multi-agent paradigm. In fact, we aim in this paper to benefit from the advantages of existing tools and techniques for each development stage and then integrate them in one unified approach. In particular approach, we advocate using Petri nets and rewriting logic to facilitate the formalization of multi-agent based systems, as well as we have integrated both the model checking and property-based testing techniques in the verification and testing stages. For illustrating the utilization and effectiveness of the proposed approach, we use it to analyze a simple automated distributing machine.
由于多智能体系统在安全关键应用中的广泛应用,其正式设计和开发在过去几十年中引起了相当大的关注。本文提出了一种基于多智能体范式的安全关键系统的高效、混合和可扩展的形式化开发方法。事实上,在本文中,我们的目标是从每个开发阶段的现有工具和技术的优势中获益,然后将它们集成在一个统一的方法中。特别是,我们提倡使用Petri网和重写逻辑来促进基于多智能体的系统的形式化,并且我们在验证和测试阶段集成了模型检查和基于属性的测试技术。为了说明该方法的实用性和有效性,我们用它来分析一个简单的自动分配机。
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引用次数: 2
Software defect prediction based on weighted extreme learning machine 基于加权极值学习机的软件缺陷预测
IF 0.7 Pub Date : 2020-01-01 DOI: 10.3233/mgs-200321
Jinjing Gai, Shang Zheng, Hualong Yu, Hongji Yang
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引用次数: 1
Flexibility measurement model of multi-agent systems 多智能体系统柔性测量模型
IF 0.7 Pub Date : 2020-01-01 DOI: 10.3233/mgs-200334
Rohallah Benaboud, Toufik Marir
Flexibility is considered as one of the key objectives of agent-based technology. Despite this, we still lack a fundamental understanding of just what “flexibility in multi-agent system (MAS)” really is. Two main questions must be asked. First, how do agents and MAS achieve a high degree of flexibility? Second, what makes one agent or one MAS more flexible than others agents or others MASs? This paper addresses the answer to these two questions by proposing an ontology of the flexibility property and a mathematical measurement model for this property. The proposed ontology gives a comprehensive view of the flexibility by decomposing it on several characteristics and presents several techniques for implementing each characteristic. In addition, it relates these characteristics to MAS components. The proposed model presents a set of metrics for measuring the different characteristics of the flexibility property. The proposed metrics have been applied to JADE applications using a tool developed for this purpose.
灵活性被认为是基于agent技术的关键目标之一。尽管如此,我们仍然对“多智能体系统(MAS)中的灵活性”究竟是什么缺乏基本的理解。必须提出两个主要问题。首先,agent和MAS如何实现高度的灵活性?第二,是什么让一个agent或MAS比其他agent或MASs更灵活?本文通过提出柔性属性的本体和柔性属性的数学度量模型来回答这两个问题。提出的本体通过将柔性分解为若干特征,给出了实现每个特征的若干技术,从而对柔性进行了全面的分析。此外,它还将这些特性与MAS组件联系起来。该模型提出了一组度量柔性特性不同特征的指标。已经使用为此目的开发的工具将建议的度量应用于JADE应用程序。
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引用次数: 1
Hybrid analytic hierarchy process-based quantitative satisfaction propagation in goal-oriented requirements engineering through sensitivity analysis 基于混合层次分析法的敏感性需求工程定量满意度传播
IF 0.7 Pub Date : 2020-01-01 DOI: 10.3233/mgs-200339
Sreenithya Sumesh, A. Krishna
In the early phase of Requirements Engineering (RE), Goal-Oriented Requirements Engineering (GORE) has been found to be a valuable tool. GORE plays a vital role in requirements analysis such as alternative selection decision-making process. This is carried out to determine the practicability and effectiveness of alternative approaches to arriving at quality goals. Most GORE models handle alternative selection based on an extremely coarse-grained qualitative approach, making it impossible to distinguish two alternatives. Many proposals are based on quantitative alternative choices, yet they do not offer a clear decision-making judgement. We propose a fuzzy-based quantitative approach to perform goal analysis using inter-actor dependencies in the i* framework, thereby addressing the ambiguity problems that arise in qualitative analysis. The goal analysis in the i* framework was performed by propagating the impact and weight values throughout the entire hierarchy of an actor. In this article, the Analytic Hierarchy Process (AHP) is adapted with GORE to discuss the evaluation of alternative strategies of the i* goal model of interdependent actors. By using a quantitative requirement prioritisation method such as the AHP, weights of importance are assigned to softgoals to obtain a multi-objective optimised function. The proposed hybrid method measures the degree of contribution of alternatives to the fulfillment of top softgoals. The integration of AHP with goal anlaysis helps to measure alternative options against each other based on the requirements problem. This approach also includes the sensitivity analysis, which helps to check the system behaviour for change in input parameter. Hence, it facilitates decision-making for the benefit of the requirements’ analyst. To explain the proposed solution, this paper considers a telemedicine system case study from the existing literature.
在需求工程(RE)的早期阶段,已经发现面向目标的需求工程(GORE)是一个有价值的工具。GORE在需求分析中起着至关重要的作用,例如选择决策过程。这是为了确定实现质量目标的替代方法的实用性和有效性。大多数GORE模型基于一种极其粗粒度的定性方法来处理备选方案的选择,因此不可能区分两个备选方案。许多建议都是基于定量的替代选择,但它们并没有提供明确的决策判断。我们提出了一种基于模糊的定量方法,使用i*框架中的参与者间依赖关系来执行目标分析,从而解决定性分析中出现的模糊性问题。i*框架中的目标分析是通过在参与者的整个层次结构中传播影响和权重值来执行的。本文将层次分析法(AHP)与GORE相结合,讨论相互依赖行为者i*目标模型的备选策略评价。采用层次分析法等定量需求优先排序方法,对软目标分配重要性权重,得到多目标优化函数。提出的混合方法度量备选方案对实现顶级软件目标的贡献程度。AHP与目标分析的集成有助于在需求问题的基础上相互度量备选方案。这种方法还包括灵敏度分析,它有助于检查输入参数变化的系统行为。因此,它有利于需求分析人员的决策制定。为了解释所提出的解决方案,本文考虑了现有文献中的远程医疗系统案例研究。
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引用次数: 3
Behavioral flexibility in Belief-Desire- Intention (BDI) architectures 信念-欲望-意图(BDI)架构中的行为灵活性
IF 0.7 Pub Date : 2020-01-01 DOI: 10.3233/mgs-200335
Adel Saadi, R. Maamri, Z. Sahnoun
The Belief-Desire-Intention (BDI) model is a popular approach to design flexible agents. The key ingredient of BDI model, that contributed to concretize behavioral flexibility, is the inclusion of the practical reasoning. On the other hand, researchers signaled some missing flexibility’s ingredient, in BDI model, essentially the lack of learning. Therefore, an extensive research was conducted in order to extend BDI agents with learning. Although this latter body of research is important, the key contribution of BDI model, i.e., practical reasoning, did not receive a sufficient attention. For instance, for performance reasons, some of the concepts included in the BDI model are neglected by BDI architectures. Neglecting these concepts was criticized by some researchers, as the ability of the agent to reason will be limited, which eventually leads to a more or less flexible reasoning, depending on the concepts explicitly included. The current paper aims to stimulate the researchers to re-explore the concretization of practical reasoning in BDI architectures. Concretely, this paper aims to stimulate a critical review of BDI architectures regarding the flexibility, inherent from the practical reasoning, in the context of single agents, situated in an environment which is not associated with uncertainty. Based on this review, we sketch a new orientation and some suggested improvements for the design of BDI agents. Finally, a simple experiment on a specific case study is carried out to evaluate some suggested improvements, namely the contribution of the agent’s “well-informedness” in the enhancement of the behavioral flexibility.
信念-欲望-意图(Belief-Desire-Intention, BDI)模型是设计柔性智能体的常用方法。BDI模型的关键因素是包含了实践推理,这有助于将行为灵活性具体化。另一方面,研究人员指出,在BDI模型中,缺少一些灵活性的成分,本质上是缺乏学习。因此,为了扩展BDI代理的学习能力,进行了广泛的研究。虽然后一项研究很重要,但BDI模型的关键贡献,即实践推理,并没有得到足够的重视。例如,出于性能原因,BDI体系结构忽略了BDI模型中包含的一些概念。忽视这些概念受到一些研究人员的批评,因为智能体的推理能力将受到限制,最终导致或多或少的灵活推理,取决于明确包含的概念。本文旨在激发研究者重新探索BDI架构中实践推理的具体化。具体地说,本文旨在激发对BDI架构关于灵活性的批判性审查,这是在单个代理的背景下,从实际推理中固有的,位于与不确定性无关的环境中。在此基础上,提出了BDI制剂设计的新方向和改进建议。最后,通过一个简单的案例研究来评估一些建议的改进,即agent的“良好信息”在增强行为灵活性方面的贡献。
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引用次数: 2
Parallel and fault-tolerant k-means clustering based on the actor model 基于参与者模型的并行容错k-means聚类
IF 0.7 Pub Date : 2020-01-01 DOI: 10.3233/mgs-200336
Salah Taamneh, A. Qawasmeh, A. Aljammal
K-means algorithm is a well-known unsupervised machine learning tool that aims at splitting a given dataset into a fixed number of clusters via iterative refinement approach. Running such an algorithm on today’s datasets that are characterized by its high multidimensionality and huge size requires using fault-tolerance mechanisms to mitigate the impact of possible failures. In this paper, we propose an actor-based implementation of k-means algorithm. The algorithm was made fault-tolerant by periodically saving the centroids into a stable storage during the failure-free execution, and restarting from the last saved centroids upon a failure. This was implemented in two different ways: optimistic checkpointing (blocking) and pessimistic checkpointing (non-blocking). The actor-based k-means algorithm was evaluated on a machine with eight cores. The experiments showed that the proposed algorithm scales very well as the number of workers increases, and can be up to ∼ 2x faster than a Java-thread-based implementation of k-means algorithm. The results also showed that the optimistic algorithm outperformed the pessimistic one, specifically, in the presence of competing I/O operations. Several failures were forced to occur during the execution to evaluate the performance of the fault-tolerant implementations. The experiments showed that the average amount of lost work ranged from 3–6%.
K-means算法是一种著名的无监督机器学习工具,旨在通过迭代优化的方法将给定的数据集分成固定数量的聚类。在当今的数据集上运行这样的算法,其特点是其高多维度和巨大的规模,需要使用容错机制来减轻可能的故障的影响。在本文中,我们提出了一种基于参与者的k-means算法实现。该算法通过在无故障执行过程中周期性地将质心保存到稳定的存储器中,并在出现故障时从上次保存的质心重新启动来实现容错。这是以两种不同的方式实现的:乐观检查点(阻塞)和悲观检查点(非阻塞)。基于参与者的k-means算法在一台八核机器上进行了评估。实验表明,随着工人数量的增加,所提出的算法可以很好地扩展,并且可以比基于java线程的k-means算法实现快2倍。结果还表明,乐观算法优于悲观算法,特别是在存在竞争I/O操作的情况下。为了评估容错实现的性能,在执行过程中强制发生了几个故障。实验表明,平均损失的工作量在3-6%之间。
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引用次数: 1
Plan distance heuristics for task fusion in distributed temporal continuous planning 分布式时间连续规划中任务融合的规划距离启发式算法
IF 0.7 Pub Date : 2020-01-01 DOI: 10.3233/MGS-200327
Gilberto Marcon dos Santos, J. Adams
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引用次数: 1
期刊
Multiagent and Grid Systems
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