323 research outputs found
On automated sequential steady-state simulation.
The credibility of the final results from stochastic simulation has had limited discussion in the simulation literature so far. However, it is important that the final results from any simulations be credible. To achieve this, validation, which determines whether the conceptual simulation model is an accurate representation of the system under study, has to be done carefully. Additionally, a proper statistical analysis of simulation output data, including a confidence interval or other assessment of statistical errors, has to be conducted before any valid inferences or conclusions about the performance of simulated dynamic systems, such as for example telecommunication networks, are made.
There are many other issues, such as choice of a good pseudo-random number generator, elimination of initialisation bias in steady-state simulations, and consideration of auto correlations in collected observations, which have to be appropriately addressed for the final results to be credible. However, many of these issues are not trivial, particularly for simulation users who may not be experts in these areas.
As a consequence, a fully-automated simulation package, which can control all important aspects of stochastic simulation, is needed. This dissertation focuses on the following contributions to such a package for steady-state simulation: properties of confidence intervals (CIs) used in coverage analysis, heuristic rules for improving the coverage of the final CIs in practical applications, automated sequential analysis of mean values by the method of regenerative cycles, automatic detection of the initial transient period for steady-state quantile estimation, and sequential steady-state quantile estimation with the automated detection of the length of initial transient period.
One difficulty in obtaining precise estimates of a system using stochastic simulation can be the cost of the computing time needed to collect the large amount of output data required. Indeed there are situations, such as estimation of rare events, where, even assuming an appropriate statistical analysis procedure is available, the cost of collecting the number of observations needed by the analysis procedure can be prohibitively large. Fortunately, inexpensive computer network resources enable computationally intensive simulations by allowing us to run parallel and distributed simulations. Therefore, where possible, we extend the contributions to the distributed stochastic simulation scenario known as the Multiple Replications In Parallel (MRIP), in which multiple processors run their own independent replications of the simulated system but cooperate with central analysers that collect data to estimate the final results
An assigned responsibility system for robotic teleoperation control
This paper proposes an architecture that explores a gap in the spectrum of existing strategies for robot control mode switching in adjustable autonomy. In situations where the environment is reasonably known and/or predictable, pre-planning these control changes could relieve robot operators of the additional task of deciding when and how to switch. Such a strategy provides a clear division of labour between the automation and the human operator(s) before the job even begins, allowing for individual responsibilities to be known ahead of time, limiting confusion and allowing rest breaks to be planned. Assigned Responsibility is a new form of adjustable autonomy-based teleoperation that allows the selective inclusion of automated control elements at key stages of a robot operation plan’s execution. Progression through these stages is controlled by automatic goal accomplishment tracking. An implementation is evaluated through engineering tests and a usability study, demonstrating the viability of this approach and offering insight into its potential applications
Impact of agent reliability and predictability on trust in real time human-agent collaboration
Trust is a prerequisite for effective human-agent collaboration. While past work has studied how trust relates to an agent's reliability, it has been mainly carried out in turn based scenarios, rather than during real-time ones. Previous research identified the performance of an agent as a key factor influencing trust. In this work, we posit that an agent's predictability also plays an important role in the trust relationship, which may be observed based on users' interactions. We designed a 2x2 within-groups experiment with two baseline conditions: (1) no agent (users' individual performance), and (2) near-flawless agent (upper bound). Participants took part in an interactive aiming task where they had to collaborate with different agents that varied in terms of their predictability, and were controlled in terms of their performance. Our results show that agents whose behaviours are easier to predict have a more positive impact on task performance, reliance and trust while reducing cognitive workload. In addition, we modelled the human-agent trust relationship and demonstrated that it is possible to reliably predict users' trust ratings using real-time interaction data. This work seeks to pave the way for the development of trust-aware agents capable of adapting and responding more appropriately to users
From presence to consciousness through virtual reality
Immersive virtual environments can break the deep, everyday connection between where our senses tell us we are and where we are actually located and whom we are with. The concept of 'presence' refers to the phenomenon of behaving and feeling as if we are in the virtual world created by computer displays. In this article, we argue that presence is worthy of study by neuroscientists, and that it might aid the study of perception and consciousness
An Action-Based Approach to Presence: Foundations and Methods
This chapter presents an action-based approach to presence. It starts by briefly describing the theoretical and empirical foundations of this approach, formalized into three key notions of place/space, action and mediation. In the light of these notions, some common assumptions about presence are then questioned: assuming a neat distinction between virtual and real environments, taking for granted the contours of the mediated environment and considering presence as a purely personal state. Some possible research topics opened up by adopting action as a unit of analysis are illustrated. Finally, a case study on driving as a form of mediated presence is discussed, to provocatively illustrate the flexibility of this approach as a unified framework for presence in digital and physical environment
Telepresence and the Role of the Senses
The telepresence experience can be evoked in a number of ways. A well-known example is a player of videogames who reports about a telepresence experience, a subjective experience of being in one place or environment, even when physically situated in another place. In this paper we set the phenomenon of telepresence into a theoretical framework. As people react subjectively to stimuli from telepresence, empirical studies can give more evidence about the phenomenon. Thus, our contribution is to bridge the theoretical with the empirical. We discuss theories of perception with an emphasis on Heidegger, Merleau-Ponty and Gibson, the role of the senses and the Spinozian belief procedure. The aim is to contribute to our understanding of this phenomenon. A telepresence-study that included the affordance concept is used to empirically study how players report sense-reactions to virtual sightseeing in two cities. We investigate and explore the interplay of the philosophical and the empirical. The findings indicate that it is not only the visual sense that plays a role in this experience, but all senses
Facing the New Technology Landscape in the Maritime Domain: Knowledge Mobilisation, Networks and Management in Human-Machine Collaboration
Rapid development in artificial intelligence and big data analytic applications have had a significant impact on knowledge mobilisation across industries including the shipping domain. This paper uses the practice of energy efficiency onboard ships as a case study to discuss how knowledge mobilisation should address this context change and uncovers how existing knowledge networks in the shipping industry would evolve in this emerging data-driven ecology. From a systems perspective, it suggests hallmarks associated with the knowledge mobilisation processes in the new technology landscape. This paper shapes a discussion to derive design and management implications of the system infrastructure contributing to a safe, efficient and sustainable shipping business model and provide insights on knowledge adaption in the emerging human-machine collaboration context
All different or all the same? Exploring the diversity of professional practices in Portuguese school psychology
"Published online: 29 March 2016"Studies have generally characterized school psychologists as a relative homogenous population. Understanding the differences in professional practices and related variables is important for the development of the profession. Using a sample of 446 Portuguese school psychologists, this study used cluster analysis to identify distinct profiles of professional activity, based on practitioners’ time distribution among different target audiences (i.e.,students, parents, teachers, school board members, school non-professional staff, and other professionals within the school community). Three distinct profiles emerged from the data: a group highly oriented to work with students, a group that distributes time almost equitably
between adults and students, and a group that concentrates attention and professional expertise on adults. Practice setting variables, such as school-psychologists-to-student ratio, schoolpsychologists-to-school ratio, number of referrals per year, and school community level of demand for different activities, were found to be significantly related to cluster membership. No personal- or professional-background-related variables differentiated the three groups. The main implications of these findings are discussed in light of recent literature regarding the models of service delivery for school psychologists
An Integrated Decision Making Approach for Adaptive Shared Control of Mobility Assistance Robots
© 2016, Springer Science+Business Media Dordrecht. Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots
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