55,252 research outputs found

    A Trust Model Based on Cloud Model and Bayesian Networks

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    Abstractthe Internet has been becoming the most important infrastructure for distributed applications which are composed of online services. In such open and dynamic environment, service selection becomes a challenge. The approaches based on subjective trust models are more adaptive and efficient than traditional binary logic based approaches. Most well known trust models use probability or fuzzy set theory to hold randomness or fuzziness respectively. Only cloud model based models consider both aspects of uncertainty. Although cloud model is ideal for representing trust degrees, it is not efficient for context aware trust evaluation and dynamic updates. By contrast, Bayesian network as an uncertain reasoning tool is more efficient for dynamic trust evaluation. An uncertain trust model that combines cloud model and Bayesian network is proposed in this paper

    Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems

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    This paper presents a human-robot trust integrated task allocation and motion planning framework for multi-robot systems (MRS) in performing a set of tasks concurrently. A set of task specifications in parallel are conjuncted with MRS to synthesize a task allocation automaton. Each transition of the task allocation automaton is associated with the total trust value of human in corresponding robots. Here, the human-robot trust model is constructed with a dynamic Bayesian network (DBN) by considering individual robot performance, safety coefficient, human cognitive workload and overall evaluation of task allocation. Hence, a task allocation path with maximum encoded human-robot trust can be searched based on the current trust value of each robot in the task allocation automaton. Symbolic motion planning (SMP) is implemented for each robot after they obtain the sequence of actions. The task allocation path can be intermittently updated with this DBN based trust model. The overall strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask automata

    A trust and reputation model based on bayesian network for web services

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    Trust and reputation for web services emerges as an important research issue in web service selection. Current web service trust models either do not integrate different important sources of trust (subjective and objective for example), or do not focus on satisfying different user’s requirements about different quality of service (QoS) attributes such as performance, availability etc. In this paper, we propose a Bayesian network trust and reputation model for web services that can overcome such limitations by considering several factors when assessing web services’ trust: direct opinion from the truster, user rating (subjective view) and QoS monitoring information (objective view). Our comprehensive approach also addresses the problems of users’ preferences and multiple QoSbased trust by specifying different conditions for the Bayesian network and targets at building a reasonable credibility model for the raters of web services

    Quantitative Measures of Regret and Trust in Human-Robot Collaboration Systems

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    Human-robot collaboration (HRC) systems integrate the strengths of both humans and robots to improve the joint system performance. In this thesis, we focus on social human-robot interaction (sHRI) factors and in particular regret and trust. Humans experience regret during decision-making under uncertainty when they feel that a better result could be obtained if chosen differently. A framework to quantitatively measure regret is proposed in this thesis. We embed quantitative regret analysis into Bayesian sequential decision-making (BSD) algorithms for HRC shared vision tasks in both domain search and assembly tasks. The BSD method has been used for robot decision-making tasks, which however is proved to be very different from human decision-making patterns. Instead, regret theory qualitatively models human\u27s rational decision-making behaviors under uncertainty. Moreover, it has been shown that joint performance of a team will improve if all members share the same decision-making logic. Trust plays a critical role in determining the level of a human\u27s acceptance and hence utilization of a robot. A dynamic network based trust model combing the time series trust model is first implemented in a multi-robot motion planning task with a human-in-the-loop. However, in this model, the trust estimates for each robot is independent, which fails to model the correlative trust in multi-robot collaboration. To address this issue, the above model is extended to interdependent multi-robot Dynamic Bayesian Networks

    A Model of Trust, Moods, and Emotions in Multiagent Systems and its Empirical Evaluation

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    Abstract We study the interplay of moods, emotions, and trust in decisionmaking contexts characterized by commitments among agents. We develop a general approach representing the relationships among these concepts via a Bayesian network model. Our approach incorporates insights from the literature and provides a computational methodology for identifying improved Bayesian models. Based on observations from an empirical study, we motivate a refined Bayesian model involving the above-mentioned concepts that goes beyond the relationships known in the literature. Our findings include (1) the violation of a commitment affects trust more than its satisfaction; (2) goal satisfaction affects mood and emotion more than commitment satisfaction, but the outcome of a commitment affects trust more than the outcome of a goal; and (3) an agent's prior mood and trust affect whether it satisfies its commitments

    Trust model for certificate revocation in Ad hoc networks

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    In this paper we propose a distributed trust model for certificate revocation in Adhoc networks. The proposed model allows trust to be built over time as the number of interactions between nodes increase. Furthermore, trust in a node is defined not only in terms of its potential for maliciousness, but also in terms of the quality of the service it provides. Trust in nodes where there is little or no history of interactions is determined by recommendations from other nodes. If the nodes in the network are selfish, trust is obtained by an exchange of portfolios. Bayesian networks form the underlying basis for this model

    A Model of Consistent Node Types in Signed Directed Social Networks

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    Signed directed social networks, in which the relationships between users can be either positive (indicating relations such as trust) or negative (indicating relations such as distrust), are increasingly common. Thus the interplay between positive and negative relationships in such networks has become an important research topic. Most recent investigations focus upon edge sign inference using structural balance theory or social status theory. Neither of these two theories, however, can explain an observed edge sign well when the two nodes connected by this edge do not share a common neighbor (e.g., common friend). In this paper we develop a novel approach to handle this situation by applying a new model for node types. Initially, we analyze the local node structure in a fully observed signed directed network, inferring underlying node types. The sign of an edge between two nodes must be consistent with their types; this explains edge signs well even when there are no common neighbors. We show, moreover, that our approach can be extended to incorporate directed triads, when they exist, just as in models based upon structural balance or social status theory. We compute Bayesian node types within empirical studies based upon partially observed Wikipedia, Slashdot, and Epinions networks in which the largest network (Epinions) has 119K nodes and 841K edges. Our approach yields better performance than state-of-the-art approaches for these three signed directed networks.Comment: To appear in the IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), 201

    A Modified Decomposed Theory of Planned Behaviour Model to Analyze User Intention towards Distance-Based Electronic Toll Collection Services

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    This study proposes a modified decomposed theory of planned behaviour model (DTPB) that integrates satisfaction and trust into the original DTPB model to explore what kind of factors affect the user intention towards distance-based electronic toll collection (ETC) services. The proposed model is empirically tested by using data collected from a questionnaire survey with a computer assisted telephone interview system. Empirical analysis is carried out in three stages that combine confirmatory factor analysis, structural equation modelling (SEM), and Bayesian network: (1) examination of reliability and validity of the measurement model; (2) analysis of structural model; (3) prediction of the probability of user intention change based on rigorous framework of SEM. The results confirm that the satisfaction and trust have positive effects on the behaviour intention, also validating that five constructs have indirect effects on the behaviour intention through attitude and perceived behaviour control. Compatibility is the most important influence factor, followed by perceived usefulness, facilitating conditions, self-efficacy, and perceived ease of use. The findings of this study identify potential improvements for ETC operator, such as contributing to the society to enhance the company image and trust of enterprise with charity activities, and simultaneously upgrading the information platform of website, software, and Apps.</p

    Trust and reputation management in decentralized systems

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    In large, open and distributed systems, agents are often used to represent users and act on their behalves. Agents can provide good or bad services or act honestly or dishonestly. Trust and reputation mechanisms are used to distinguish good services from bad ones or honest agents from dishonest ones. My research is focused on trust and reputation management in decentralized systems. Compared with centralized systems, decentralized systems are more difficult and inefficient for agents to find and collect information to build trust and reputation. In this thesis, I propose a Bayesian network-based trust model. It provides a flexible way to present differentiated trust and combine different aspects of trust that can meet agents’ different needs. As a complementary element, I propose a super-agent based approach that facilitates reputation management in decentralized networks. The idea of allowing super-agents to form interest-based communities further enables flexible reputation management among groups of agents. A reward mechanism creates incentives for super-agents to contribute their resources and to be honest. As a single package, my work is able to promote effective, efficient and flexible trust and reputation management in decentralized systems
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