4,736 research outputs found

    Customer-engineer relationship management for converged ICT service companies

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    Thanks to the advent of converged communications services (often referred to as ‘triple play’), the next generation Service Engineer will need radically different skills, processes and tools from today’s counterpart. Why? in order to meet the challenges of installing and maintaining services based on multi-vendor software and hardware components in an IP-based network environment. The converged services environment is likely to be ‘smart’ and support flexible and dynamic interoperability between appliances and computing devices. These radical changes in the working environment will inevitably force managers to rethink the role of Service Engineers in relation to customer relationship management. This paper aims to identify requirements for an information system to support converged communications service engineers with regard to customer-engineer relationship management. Furthermore, an architecture for such a system is proposed and how it meets these requirements is discussed

    Multi-agent systems for power engineering applications - part 1 : Concepts, approaches and technical challenges

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    This is the first part of a 2-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part 1 of the paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part 2 of the paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented

    Flexible Task coordination for mobile workforce

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    With the advancement of networking and mobile devices, more and more mobile business processes are automated and supported using the technologies. Mobile businesses processes are naturally exposed to uncertainty and dynamic changes that require distributed coordination. In large business organizations, the complexity of the processes also makes central control difficult due to the large number of variables to consider and mobile workers involved. To this end, this paper presents a flexible coordination mechanism for mobile workforce where multiple task assignment models are used together to adapt to dynamic changes and achieve efficiency. The overall system is flexible in that the assignment models are easily added because they are constructed as components, and the switch between assignment models are easy using manual or automated transition between the models. An example application of the model is presented using a real telecommunication organization in Europe where field workers install and repair telecommunication networks for customers

    On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

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    This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p

    One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI

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    Conversational agents have been gaining increasing popularity in recent years. Influenced by the widespread adoption of task-oriented agents such as Apple Siri and Amazon Alexa, these agents are being deployed into various applications to enhance user experience. Although these agents promote "ask me anything" functionality, they are typically built to focus on a single or finite set of expertise. Given that complex tasks often require more than one expertise, this results in the users needing to learn and adopt multiple agents. One approach to alleviate this is to abstract the orchestration of agents in the background. However, this removes the option of choice and flexibility, potentially harming the ability to complete tasks. In this paper, we explore these different interaction experiences (one agent for all) vs (user choice of agents) for conversational AI. We design prototypes for each, systematically evaluating their ability to facilitate task completion. Through a series of conducted user studies, we show that users have a significant preference for abstracting agent orchestration in both system usability and system performance. Additionally, we demonstrate that this mode of interaction is able to provide quality responses that are rated within 1% of human-selected answers
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