Shinren : Non-monotonic trust management for distributed systems

The open and dynamic nature of modern distributed systems and pervasive environments presents significant challenges to security management. One solution may be trust management which utilises the notion of trust in order to specify and interpret security policies and make decisions on security-related actions. Most trust management systems assume monotonicity where additional information can only result in the increasing of trust. The monotonic assumption oversimplifies the real world by not considering negative information, thus it cannot handle many real world scenarios. In this paper we present Shinren, a novel non-monotonic trust management system based on bilattice theory and the anyworld assumption. Shinren takes into account negative information and supports reasoning with incomplete information, uncertainty and inconsistency. Information from multiple sources such as credentials, recommendations, reputation and local knowledge can be used and combined in order to establish trust. Shinren also supports prioritisation which is important in decision making and resolving modality conflicts that are caused by non-monotonicity

BUILDING TRUST FOR SERVICE ASSESSMENT IN INTERNET-ENABLED COLLABORATIVE PRODUCT DESIGN & REALIZATION ENVIRONMENTS

Reducing costs, increasing speed and leveraging the intelligence of partners involved during product design processes are important benefits of Internet-enabled collaborative product design and realization environments. The options for cost-effective product design, re-design or improvement are at their peak during the early stages of the design process and designers can collaborate with suppliers, manufacturers and other relevant contributors to acquire a better understanding of associated costs and product viability. Collaboration is by no means a new paradigm. However, companies have found distrust of collaborative partners to be the most intractable obstacle to collaborative commerce and Internet-enabled business especially in intellectual property environments, which handle propriety data on a constant basis. This problem is also reinforced in collaborative environments that are distributed in nature. Thus trust is the main driver or enabler of successful collaborative efforts or transactions in Internet-enabled product design environments. Focus is on analyzing the problem of ¡®trust for services¡¯ in distributed collaborative service provider assessment and selection, concentrating on characteristics specific to electronic product design (e-Design) environments. Current tools for such collaborative partner/provider assessment are inadequate or non-existent and researching network, user, communication and service trust problems, which hinder the growth and acceptance of true collaboration in product design, can foster new frontiers in manufacturing, business and technology. Trust and its associated issues within the context of a secure Internet-enabled product design & realization platform is a multifaceted and complex problem, which demands a strategic approach crossing disciplinary boundaries. A Design Environment Trust Service (DETS) framework is proposed to incorporate trust for services in product design environments based on client specified (or default) criteria. This involves the analysis of validated network (objective) data and non-network (subjective) data and the use of Multi Criteria Decision Making (MCDM) methodology for the selection of the most efficient service provision alternative through the minimization of distance from a specified ideal point and interpreted as a Dynamic (Design) Trust Index (DTI) or rank. Hence, the service requestor is provided with a quantifiable degree of belief to mitigate information asymmetry and enable knowledgeable decision-making regarding trustworthy service provision in a distributed environment

Computational Modeling of Trust Factors Using Reinforcement Learning

As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous decision-making algorithms. The approach relies on the implementation of a neural network based reinforcement learning paradigm known as adaptive critic design to model an adaptive decision making process that is regulated by a quantitative measure of risk associated with each possible decision. Specifically, this work expands on the risk-directed exploration strategies of reinforcement learning to obtain quantitative risk factors for an automated object recognition process in the presence of imprecise data. Accordingly, this work addresses the challenge of automated risk quantification based on the confidence of the decision model and the nature of given data. Additionally, further analysis into risk directed policy development for improved object recognition is presented

Self-interested service-oriented agents based on trust and QoS for dynamic reconfiguration

Progressively increasing complexity of dynamic environments, in which services and applications are demanded by potential clients, requires a high level of reconfiguration of the offer to better match that ever changing demand. In particular, the dynamic change of the client’s needs, leading to higher exigency, may require a smart and flexible automatic composition of more elementary services. By leveraging the service-oriented architectures and multi-agent system benefits, the paper proposes a method to explore the flexibility of the decision support for the services’ reconfiguration based on several pillars, such as trust, reputation and QoS models, which allows the selection based on measuring the expected performance of the agents. Preliminary experimental results, extracted from a real case scenario, allow highlighting the benefits of the proposed distributed and flexible solution to balance the workload of service providers in a simple and fast manner. The proposed solution includes the agents’ intelligent decision-making capability to dynamically and autonomously change services selection on the fly, towards more trustworthy services with better quality when unexpected events happen, e.g. broken machines. We then propose the use of competitive self-interested agents to provide services that best suits to the client through dynamic service composition.info:eu-repo/semantics/publishedVersio

Multicriteria Consensus Models to Support Intelligent Group Decision-Making

The development of intelligent systems is progressing rapidly, thanks to advances in information technology that enable collective, automated, and effective decision-making based on information collected from diverse sources. Group decision-making (GDM) is a key part of intelligent decision-making (IDM), which has received considerable attention in recent years. IDM through GDM refers to a decision-making problem where a group of intelligent decision-makers (DMs) evaluate a set of alternatives with respect to specific attributes. Intelligent communication among DMs aims to give orders to the available alternatives. However, GDM models developed for IDM must incorporate consensus support models to effectively integrate input from each DM into the final decision. Many efforts have been made to design consensus models to support IDM, depending on the decision problem or environment. Despite promising results, significant gaps remain in research on the design of such support models. One major drawback of existing consensus models is their dependence on the type of decision environment, making them less generalizable. Moreover, these models are often static and cannot respond to dynamic changes in the decision environment. Another limitation is that consensus models for large-scale decision environments lack an efficient communication regime to enable DM interactions. To address these challenges, this dissertation proposes developing consensus models to support IDM through GDM. To address the generalization issue of existing consensus models, reinforcement learning (RL) is proposed. RL agents can be built on the Markov decision process to enable IDM, potentially removing the generalization issue of consensus support models. Contrary to most consensus models, which assume static decision environments, this dissertation proposes a computationally efficient dynamic consensus model to support dynamic IDM. Finally, to facilitate secure and efficient interactions among intelligent DMs in large-scale problems, Blockchain technology is proposed to speed up the consensus process. The proposed communication regime also includes trust-building mechanisms that employ Blockchain protocols to remove enduring and limitative assumptions on opinion similarity among agents

Privacy, security, and trust issues in smart environments

Recent advances in networking, handheld computing and sensor technologies have driven forward research towards the realisation of Mark Weiser's dream of calm and ubiquitous computing (variously called pervasive computing, ambient computing, active spaces, the disappearing computer or context-aware computing). In turn, this has led to the emergence of smart environments as one significant facet of research in this domain. A smart environment, or space, is a region of the real world that is extensively equipped with sensors, actuators and computing components [1]. In effect the smart space becomes a part of a larger information system: with all actions within the space potentially affecting the underlying computer applications, which may themselves affect the space through the actuators. Such smart environments have tremendous potential within many application areas to improve the utility of a space. Consider the potential offered by a smart environment that prolongs the time an elderly or infirm person can live an independent life or the potential offered by a smart environment that supports vicarious learning

Delivering services by building and running virtual organisations

Non peer reviewedPostprin

Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges

Human-swarm interaction (HSI) involves a number of human factors impacting human behaviour throughout the interaction. As the technologies used within HSI advance, it is more tempting to increase the level of swarm autonomy within the interaction to reduce the workload on humans. Yet, the prospective negative effects of high levels of autonomy on human situational awareness can hinder this process. Flexible autonomy aims at trading-off these effects by changing the level of autonomy within the interaction when required; with mixed-initiatives combining human preferences and automation's recommendations to select an appropriate level of autonomy at a certain point of time. However, the effective implementation of mixed-initiative systems raises fundamental questions on how to combine human preferences and automation recommendations, how to realise the selected level of autonomy, and what the future impacts on the cognitive states of a human are. We explore open challenges that hamper the process of developing effective flexible autonomy. We then highlight the potential benefits of using system modelling techniques in HSI by illustrating how they provide HSI designers with an opportunity to evaluate different strategies for assessing the state of the mission and for adapting the level of autonomy within the interaction to maximise mission success metrics.Comment: Author version, accepted at the 2018 IEEE Annual Systems Modelling Conference, Canberra, Australi

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure