基于可信度的MANETs动态信任评估

移动Ad Hoc网由于没有固定基础设施,使得网络节点间的信任问题变得日益突出。针对已有信任评估模型在证据收集和信任融合方面的不足,提出了一个多维证据融合信任评估模型。通过拓展Watchdog机制,检测节点对包的各种历史处理行为,来扩宽直接信任证据收集的维度;同时使用Beta分布下的贝叶斯统计方法,计算每种行为的可信度,之后通过证据合成规则来融合多种行为,以提高信任评估的准确性。通过仿真实验,验证了该模型的有效性;分析表明,模型中使用信息贴近度量化推荐权重,降低了恶意推荐带来的影响,保证了信任评估的健壮性。     

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．     

An integrated trust and reputation model for open multi-agent systems

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent’s likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent’s environment.     

Fuzzy computational models for trust and reputation systems

In the recent past, a considerable research has been devoted to trust and reputation mechanisms to simplify complex transactions for open environments in social networking, e-commerce, and recommender systems (RS). In real life, we come to know about others through our social circle according to their reputation which is a public view. However, it is not always adequate to depend solely on the public view and therefore a trust measure is required to give a personalized view of the future encounters with a specific partner. In this paper, we propose fuzzy computational models for both trust and reputation concepts. Reciprocity and experience are used for trust modeling while the proposed reputation model is a fuzzy extension of beta reputation model. A two-level filtering methodology is proposed to benefit to a large extent from both the concepts separately. In order to justify the proposed models, we compared them with the existing reputation models for movie RS. The experimental results show that the incorporation of trust and reputation concepts into RS indeed improves the recommendation accuracy and establish that our models are better than beta and the popular eBay reputation models.     

Computational trust and reputation models for open multi-agent systems: a review

In open environments, agents depend on reputation and trust mechanisms to evaluate the behavior of potential partners. The scientific research in this field has considerably increased, and in fact, reputation and trust mechanisms have been already considered a key elements in the design of multi-agent systems. In this paper we provide a survey that, far from being exhaustive, intends to show the most representative models that currently exist in the literature. For this enterprise we consider several dimensions of analysis that appeared in three existing surveys, and provide new dimensions that can be complementary to the existing ones and that have not been treated directly. Moreover, besides showing the original classification that each one of the surveys provide, we also classify models that where not taken into account by the original surveys. The paper illustrates the proliferation in the past few years of models that follow a more cognitive approach, in which trust and reputation representation as mental attitudes is as important as the final values of trust and reputation. Furthermore, we provide an objective definition of trust, based on Castelfranchi’s idea that trust implies a decision to rely on someone.     

普适环境中基于模糊理论的信任评估模型

信任管理是当前普适环境中信息安全研究的热点。为了解决主观信任的模糊性和主体之间信任关系的动态性,在模糊数学中的模糊理论的基础上,提出了一个用户信任度的评估模型。该模型是将模糊理论应用到信任关系的不确定性和动态性上,对影响信任主体的主要因素进行综合评估,得出一个总的信任度来确定是否可以信任主体以便进行交互。该模型可以全面地评估用户的信任度,很大程度上保证了普适行为的安全可靠,为处理普适计算的信任管理的不确定研究提供了一个新的方法。     

An efficient trust estimation model for multi-agent systems using temporal difference learning

In multi-agent system (MAS) applications, teamwork among the agents is essential as the agents are required to collaborate and pool resources to execute the given tasks and complete the objectives successfully. A vital part of the collaboration is sharing of information and resources in order to optimize their efforts in achieving the given objectives. Under such collaborative environment, trust among the agents plays a critical role to ensure efficient cooperation. This study looks into developing a trust evaluation model that can empirically evaluate the trust of one agent on the other. The proposed model is developed using temporal difference learning method, incorporating experience gained through interactions into trust evaluation. Simulation experiments are conducted to evaluate the performance of the developed model against some of the most recent models reported in the literature. The results of the simulation experiments indicate that the proposed model performs better than the comparison models in estimating trust more effectively.

     

模糊Petri网及其在Web服务信任评估中的应用*

为了提高 Web服务的信任评估准确度和交互成功率,提出了一种新的基于模糊Petri网的Web服务信任评估模型(WS-TEM)。该模型给出了评估的模糊信任规则,在此基础上,提出了一种基于模糊Petri网的推理算法,并使用模糊Petri网中知识表示和运行的基本算法,并行处理一个服务实体的多个推荐信任来综合评估Web服务实体间的信任关系。经过模拟实验和分析表明,该模型在Web服务信任评估上更加准确可靠,从而降低了交互的风险,保证了交互的成功率。     

Robotics software frameworks for multi-agent robotic systems development

Robotics is an area of research in which the paradigm of Multi-Agent Systems (MAS) can prove to be highly useful. Multi-Agent Systems come in the form of cooperative robots in a team, sensor networks based on mobile robots, and robots in Intelligent Environments, to name but a few. However, the development of Multi-Agent Robotic Systems (MARS) still presents major challenges. Over the past decade, a high number of Robotics Software Frameworks (RSFs) have appeared which propose some solutions to the most recurrent problems in robotics. Some of these frameworks, such as ROS, YARP, OROCOS, ORCA, Open-RTM, and Open-RDK, possess certain characteristics and provide the basic infrastructure necessary for the development of MARS. The contribution of this work is the identification of such characteristics as well as the analysis of these frameworks in comparison with the general-purpose Multi-Agent System Frameworks (MASFs), such as JADE and Mobile-C.     

Fuzzy consensus of multi-agent systems with granular fuzzy uncertainty in communication topology

In this paper, the consensus problem in linear multi-agent systems with uncertain topology is investigated. The uncertainty in the communication topology is represented by granular fuzzy numbers obtained from expert knowledge, and the required calculations are performed with multidimensional relative–distance–measurement interval arithmetic. The concept of granular fuzzy graph and fuzzy Gershgorin disc theorem is introduced. Then a fuzzy consensus protocol is proposed for the multi-agent system with fuzzy granular uncertain topology. A two-step algorithm is also proposed to build the consensus protocol. We also propose a theorem for calculating the final value of fuzzy consensus, which shows the final value of consensus as a fuzzy number. Simulation results show the efficiency of the suggested algorithm.     

MOBMAS: A methodology for ontology-based multi-agent systems development

Ontologies offer significant benefits to multi-agent systems: interoperability, reusability, support for multi-agent system (MAS) development activities (such as system analysis and agent knowledge modeling) and support for MAS operation (such as agent communication and reasoning). This paper presents an ontology-based methodology, MOBMAS, for the analysis and design of multi-agent systems. MOBMAS is the first methodology that explicitly identifies and implements the various ways in which ontologies can be used in the MAS development process and integrated into the MAS model definitions. In this paper, we present comprehensive documentation and validation of MOBMAS.     

Model-driven engineering techniques for the development of multi-agent systems

Model-driven engineering (MDE), implicitly based upon meta-model principles, is gaining more and more attention in software systems due to its inherent benefits. Its use normally improves the quality of the developed systems in terms of productivity, portability, inter-operability and maintenance. Therefore, its exploitation for the development of multi-agent systems (MAS) emerges in a natural way. In this paper, agent-oriented software development (AOSD) and MDE paradigms are fully integrated for the development of MAS. Meta-modeling techniques are explicitly used to speed up several phases of the process. The Prometheus methodology is used for the purpose of validating the proposal. The meta-object facility (MOF) architecture is used as a guideline for developing a MAS editor according to the language provided by Prometheus methodology. Firstly, an Ecore meta-model for Prometheus language is developed. Ecore is a powerful tool for designing model-driven architectures (MDA). Next, facilities provided by the Graphical Modeling Framework (GMF) are used to generate the graphical editor. It offers support to develop agent models conform to the meta-model specified. Afterwards, it is also described how an agent code generator can be developed. In this way, code is automatically generated using as input the model specified with the graphical editor. A case of study validates the method put in practice for the development of a multi-agent surveillance system.     

基于角色和信誉的跨域信任管理模型研究

目前,基于角色的信任管理及基于信誉的信任管理模型都存在安全缺陷。本文将基于角色的信任管理和基于信誉的信任管理相结合,提出了一种改进的信任管理模型,以提高跨域系统的安全性。     

Fuzzy adaptive dynamic programming-based optimal leader-following consensus for heterogeneous nonlinear multi-agent systems

In this paper, a novel online iterative scheme, based on fuzzy adaptive dynamic programming, is proposed for distributed optimal leader-following consensus of heterogeneous nonlinear multi-agent systems under directed communication graph. This scheme combines game theory, adaptive dynamic programming together with generalized fuzzy hyperbolic model (GFHM). Firstly, based on precompensation technique, an appropriate model transformation is proposed to convert the error system into augmented error system, and an exquisite performance index function is defined for this system. Secondly, on the basis of Hamilton–Jacobi–Bellman (HJB) equation, the optimal consensus control is designed and a novel policy iteration (PI) algorithm is put forward to learn the solutions of the HJB equation online. Here, the proposed PI algorithm is implemented on account of GFHMs. Compared with dual-network model including critic network and action network, the proposed scheme only requires critic network. Thirdly, the augmented consensus error of each agent and the weight estimation error of each GFHM are proved to be uniformly ultimately bounded, and the stability of our method has been verified. Finally, some numerical examples and application examples are conducted to demonstrate the effectiveness of the theoretical results.

     

Design,development and evaluation of a human-computer trust scale

ABSTRACT

New technologies, data, and algorithms impact nearly every aspect of daily life. Unfortunately, many of these algorithms operate like black boxes and cannot explain their results even to their programmers, let alone to end-users. As more and more tasks get delegated to such intelligent systems and the nature of user interactions with them becomes increasingly complex, it is important to understand the amount of trust that a user is willing to place on such systems. However, attempts at quantifying trust have either been limited in their scope or not empirically thorough. To address this, we build on prior work which empirically modelled trust in user-technology interactions and describe the development and evolution of a human computer trust scale. We present results of two studies (N=118 & N=183) which were undertaken to assess the reliability and validity of the proposed scale. Our study contributes to the literature by (a) developing a multi-dimensional scale to assess user trust in HCI and (b) being the first study to use the concept of design fiction and future scenarios to study trust.     

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.     

基于可信性理论的主观信任模型研究

通过对开放网络环境中主观信任评价方法的讨论,引入可信性理论来研究主观信任的模糊性,并提出了极大化期望值准则下的主观信任量化评价方法。针对主体信任属性评价值以及评价权重均为模糊变量的情形,给出了主观信任模糊期望值的模拟计算方法。实例分析表明,该方法对主观信任评价是可行和有效的。