Information uncertainty is inherent in many real-world problems and adds a layer of complexity to modeling and visualization tasks. This often causes users to ignore uncertainty, especially when it comes to visualization, thereby discarding valuable knowledge. A coherent framework for the modeling and visualization of information uncertainty is needed to address this issue In this work, we have identified four major barriers to the uptake of uncertainty modeling and visualization. Firstly, there are numerous uncertainty modeling tech- niques and users are required to anticipate their uncertainty needs before building their data model. Secondly, parameters of uncertainty tend to be treated at the same level as variables making it easy to introduce avoidable errors. This causes the uncertainty technique to dictate the structure of the data model. Thirdly, propagation of uncertainty information must be manually managed. This requires user expertise, is error prone, and can be tedious. Finally, uncertainty visualization techniques tend to be developed for particular uncertainty types, making them largely incompatible with other forms of uncertainty information. This narrows the choice of visualization techniques and results in a tendency for ad hoc uncertainty visualization. The aim of this thesis is to present an integrated information uncertainty modeling and visualization environment that has the following main features: information and its uncertainty are encapsulated into atomic variables, the propagation of uncertainty is automated, and visual mappings are abstracted from the uncertainty information data type. Spreadsheets have previously been shown to be well suited as an approach to visu- alization. In this thesis, we devise a new paradigm extending the traditional spreadsheet to intrinsically support information uncertainty.Our approach is to design a framework that integrates uncertainty modeling tech- niques into a hierarchical order based on levels of detail. The uncertainty information is encapsulated and treated as a unit allowing users to think of their data model in terms of the variables instead of the uncertainty details. The system is intrinsically aware of the encapsulated uncertainty and is therefore able to automatically select appropriate uncertainty propagation methods. A user-objectives based approach to uncertainty visualization is developed to guide the visual mapping of abstracted uncertainty information. Two main abstractions of uncertainty information are explored for the purpose of visual mapping: the Unified Uncertainty Model and the Dual Uncertainty Model. The Unified Uncertainty Model provides a single view of uncertainty for visual mapping, whereas the Dual Uncertainty Model distinguishes between possibilistic and probabilistic views. Such abstractions provide a buffer between the visual mappings and the uncertainty type of the underly- ing data, enabling the user to change the uncertainty detail without causing the visual- ization to fail. Two main case studies are presented. The first case study covers exploratory and forecasting tasks in a business planning context. The second case study inves- tigates sensitivity analysis for financial decision support. Two minor case studies are also included: one to investigate the relevancy visualization objective applied to busi- ness process specifications, and the second to explore the extensibility of the system through General Purpose Graphics Processor Unit (GPGPU) use. A quantitative anal- ysis compares our approach to traditional analytical and numerical spreadsheet-based approaches. Two surveys were conducted to gain feedback on the from potential users. The significance of this work is that we reduce barriers to uncertainty modeling and visualization in three ways. Users do not need a mathematical understanding of the uncertainty modeling technique to use it; uncertainty information is easily added, changed, or removed at any stage of the process; and uncertainty visualizations can be built independently of the uncertainty modeling technique.

No description available.

Uncertainty is, it seems, more or less constantly present in our lives. Even so, grasping the concept philosophically is far from trivial. In this doctoral thesis, uncertainty and its conceptual companion information are studied. Axiomatic analyses are provided and numerical measures suggested. In addition to these basic conceptual analyses, the widespread practice of so-called safety factor use in societal regulation is analyzed along with the interplay between science and policy in European regulation of chemicals and construction. / QC 20120202

The purpose of this research is to develop a method for selecting the fidelity of contributing analyses in computer simulations. Model uncertainty is a significant component of result validity, yet it is neglected in most conceptual design studies. When it is considered, it is done so in only a limited fashion, and therefore brings the validity of selections made based on these results into question. Neglecting model uncertainty can potentially cause costly redesigns of concepts later in the design process or can even cause program cancellation. Rather than neglecting it, if one were to instead not only realize the model uncertainty in tools being used but also use this information to select the tools for a contributing analysis, studies could be conducted more efficiently and trust in results could be quantified. Methods for performing this are generally not rigorous or traceable, and in many cases the improvement and additional time spent performing enhanced calculations are washed out by less accurate calculations performed downstream. The intent of this research is to resolve this issue by providing a method that will minimize the amount of time spent conducting computer simulations while meeting accuracy and concept resolution requirements for results.

Master of Science / Department of Computing and Information Sciences / Xinming (Simon) Ou / A well-known problem in current intrusion detection tools is that they create too many low-level alerts and system administrators find it hard to cope up with the huge volume. Also, when they have to combine multiple sources of information to confirm an attack, there is a dramatic increase in the complexity. Attackers use sophisticated techniques to evade the detection and current system monitoring tools can only observe the symptoms or effects of malicious activities. When mingled with similar effects from normal or non-malicious behavior they lead intrusion analysis to conclusions of varying confidence and high false positive/negative rates. In this thesis work we present an empirical approach to the problem of modeling uncertainty where inferred security implications of low-level observations are captured in a simple logical language augmented with uncertainty tags. We have designed an automated reasoning process that enables us to combine multiple sources of system monitoring data and extract highly-confident attack traces from the numerous possible interpretations of low-level observations. We have developed our model empirically: the starting point was a true intrusion that happened on a campus network we studied to capture the essence of the human reasoning process that led to conclusions about the attack. We then used a Datalog-like language to encode the model and a Prolog system to carry out the reasoning process. Our model and reasoning system reached the same conclusions as the human administrator on the question of which machines were certainly compromised. We then automatically generated the reasoning model needed for handling Snort alerts from the natural-language descriptions in the Snort rule repository, and developed a Snort add-on to analyze Snort alerts. Keeping the reasoning model unchanged, we applied our reasoning system to two third-party data sets and one production network. Our results showed that the reasoning model is effective on these data sets as well. We believe such an empirical approach has the potential of codifying the seemingly ad-hoc human reasoning of uncertain events, and can yield useful tools for automated intrusion analysis.

Planning optimal paths for large numbers of robots is computationally expensive. In this thesis, we present a new framework for multirobot path planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as needed. More specifically subdimensional expansion initially creates a one-dimensional search space embedded in the joint configuration space of the multirobot system. When the search space is found to be blocked during planning by a robot-robot collision, the dimensionality of the search space is locally increased to ensure that an alternative path can be found. As a result, robots are only coordinated when necessary, which reduces the computational cost of finding a path. Subdimensional expansion is a exible framework that can be used with multiple planning algorithms. For discrete planning problems, subdimensional expansion can be combined with A* to produce the M* algorithm, a complete and optimal multirobot path planning problem. When the configuration space of individual robots is too large to be explored effectively with A*, subdimensional expansion can be combined with probabilistic planning algorithms to produce sRRT and sPRM. M* is then extended to solve variants of the multirobot path planning algorithm. We present the Constraint Manifold Subsearch (CMS) algorithm to solve problems where robots must dynamically form and dissolve teams with other robots to perform cooperative tasks. Uncertainty M* (UM*) is a variant of M* that handles systems with probabilistic dynamics. Finally, we apply M* to multirobot sequential composition. Results are validated with extensive simulations and experiments on multiple physical robots.

No description available.

International Telemetering Conference Proceedings / October 28-31, 1985 / Riviera Hotel, Las Vegas, Nevada / In many engineering applications, a systems analysis is performed to study the effects of random error propagation throughout a system. Often these errors are not independent, and have joint behavior characterized by arbitrary covariance structure. The multivariate nature of such problems is compounded in complex systems, where overall system performance is described by a q-dimensional random vector. To address this problem, a computer program was developed which generates Taylor series approximations for multivariate system performance in the presence of random component variablilty. A summary of an application of this approach is given in which an analysis was performed to assess simultaneous design margins and to ensure optimal component selection.

In a market environment that is subject to continuous changes, companies need to adapttheir production systems in order to maintain the competitive edge. Current literatureshows that with a successful production system design, higher levels of output, eciencyand quality can be achieved.However, designing a production system is done infrequently and therefore tends tolack experience. As a result, design decisions have to be made under uncertainty due toa lack of information, structure and knowledge. In fact, the success of a design process isdirectly linked to the level of uncertainty.The purpose of this thesis is to reduce uncertainty in production system design throughDiscrete Event Simulation before an assembly system is implemented. Therefore, a theoreticalstudy was carried out dening types and sources of uncertainty in productionsystem design. Parallel to the theoretical study, a case study in Volvo ConstructionEquipment Operations Hallsberg was conducted. Discrete Event Simulation was testedas a tool to reduce uncertainty in production system design.The analysis illustrates the observed sources of uncertainty in production systemdesign cover a process, organizational, corporate, market and cultural context.The relevant uncertainty types identied in the case study in Volvo ConstructionEquipment Operations Hallsberg were environmental, system, technical, structural,temporal, lack of knowledge and lack of information. The information providedby the Discrete Event Simulation in order to reduce uncertainty are in form ofKPIs, process structure and visualization. The provided information had a positiveimpact on the degree of technical uncertainties, the lack of knowledge and thelack of information. As a result, the level of uncertainty in the Volvo ConstructionEquipment Operations Hallsberg future line designing process was reduced.

To make sense of our unpredictable world, humans use sensory information streaming through billions of peripheral neurons. Uncertainty and ambiguity plague each sensory stream, yet remarkably our perception of the world is seamless, robust and often optimal in the sense of minimising perceptual variability. Moreover, humans have a remarkable capacity for dexterous manipulation. Initiation of precise motor actions under uncertainty requires awareness of not only the statistics of our environment but also the reliability of our sensory and motor apparatus. What happens when our sensory and motor systems are disrupted? Upper-limb amputees tted with a state-of-the-art prostheses must learn to both control and make sense of their robotic replacement limb. Tactile feedback is not a standard feature of these open-loop limbs, fundamentally limiting the degree of rehabilitation. This thesis introduces a modular closed-loop upper-limb prosthesis, a modified Touch Bionics ilimb hand with a custom-built linear vibrotactile feedback array. To understand the utility of the feedback system in the presence of multisensory and sensorimotor influences, three fundamental open questions were addressed: (i) What are the mechanisms by which subjects compute sensory uncertainty? (ii) Do subjects integrate an artificial modality with visual feedback as a function of sensory uncertainty? (iii) What are the influences of open-loop and closed-loop uncertainty on prosthesis control? To optimally handle uncertainty in the environment people must acquire estimates of the mean and uncertainty of sensory cues over time. A novel visual tracking experiment was developed in order to explore the processes by which people acquire these statistical estimators. Subjects were required to simultaneously report their evolving estimate of the mean and uncertainty of visual stimuli over time. This revealed that subjects could accumulate noisy evidence over the course of a trial to form an optimal continuous estimate of the mean, hindered only by natural kinematic constraints. Although subjects had explicit access to a measure of their continuous objective uncertainty, acquired from sensory information available within a trial, this was limited by a conservative margin for error. In the Bayesian framework, sensory evidence (from multiple sensory cues) and prior beliefs (knowledge of the statistics of sensory cues) are combined to form a posterior estimate of the state of the world. Multiple studies have revealed that humans behave as optimal Bayesian observers when making binary decisions in forced-choice tasks. In this thesis these results were extended to a continuous spatial localisation task. Subjects could rapidly accumulate evidence presented via vibrotactile feedback (an artificial modality ), and integrate it with visual feedback. The weight attributed to each sensory modality was chosen so as to minimise the overall objective uncertainty. Since subjects were able to combine multiple sources of sensory information with respect to their sensory uncertainties, it was hypothesised that vibrotactile feedback would benefit prosthesis wearers in the presence of either sensory or motor uncertainty. The closed-loop prosthesis served as a novel manipulandum to examine the role of feed-forward and feed-back mechanisms for prosthesis control, known to be required for successful object manipulation in healthy humans. Subjects formed economical grasps in idealised (noise-free) conditions and this was maintained even when visual, tactile and both sources of feedback were removed. However, when uncertainty was introduced into the hand controller, performance degraded significantly in the absence of visual or tactile feedback. These results reveal the complementary nature of feed-forward and feed-back processes in simulated prosthesis wearers, and highlight the importance of tactile feedback for control of a prosthesis.