Controlling system uncertainty in sport and work

An applied psychological framework for coping with performance uncertainty in sport and work systems is presented. The theme of personal control serves to integrate ideas prevalent in industrial and organisational psychology, the stress literature and labour process theory. These commonly focus on the promotion of tacit knowledge and learned resourcefulness in individual performers. Finally, data from an empirical evaluation of a development training programme to facilitate self-regulation skills in professional athletes are briefly highlighted.

RELAX:a language to address uncertainty in self-adaptive systems requirement

Self-adaptive systems have the capability to autonomously modify their behavior at run-time in response to changes in their environment. Self-adaptation is particularly necessary for applications that must run continuously, even under adverse conditions and changing requirements; sample domains include automotive systems, telecommunications, and environmental monitoring systems. While a few techniques have been developed to support the monitoring and analysis of requirements for adaptive systems, limited attention has been paid to the actual creation and specification of requirements of self-adaptive systems. As a result, self-adaptivity is often constructed in an ad-hoc manner. In order to support the rigorous specification of adaptive systems requirements, this paper introduces RELAX, a new requirements language for self-adaptive systems that explicitly addresses uncertainty inherent in adaptive systems. We present the formal semantics for RELAX in terms of fuzzy logic, thus enabling a rigorous treatment of requirements that include uncertainty. RELAX enables developers to identify uncertainty in the requirements, thereby facilitating the design of systems that are, by definition, more flexible and amenable to adaptation in a systematic fashion. We illustrate the use of RELAX on smart home applications, including an adaptive assisted living system.

Relaxing claims:coping with uncertainty while evaluating assumptions at run time

Self-adaptation enables software systems to respond to changing environmental contexts that may not be fully understood at design time. Designing a dynamically adaptive system (DAS) to cope with this uncertainty is challenging, as it is impractical during requirements analysis and design time to anticipate every environmental condition that the DAS may encounter. Previously, the RELAX language was proposed to make requirements more tolerant to environmental uncertainty, and Claims were applied as markers of uncertainty that document how design assumptions affect goals. This paper integrates these two techniques in order to assess the validity of Claims at run time while tolerating minor and unanticipated environmental conditions that can trigger adaptations. We apply the proposed approach to the dynamic reconfiguration of a remote data mirroring network that must diffuse data while minimizing costs and exposure to data loss. Results show RELAXing Claims enables a DAS to reduce adaptation costs. © 2012 Springer-Verlag.

A goal-based modeling approach to develop requirements of an adaptive system with environmental uncertainty

Dynamically adaptive systems (DASs) are intended to monitor the execution environment and then dynamically adapt their behavior in response to changing environmental conditions. The uncertainty of the execution environment is a major motivation for dynamic adaptation; it is impossible to know at development time all of the possible combinations of environmental conditions that will be encountered. To date, the work performed in requirements engineering for a DAS includes requirements monitoring and reasoning about the correctness of adaptations, where the DAS requirements are assumed to exist. This paper introduces a goal-based modeling approach to develop the requirements for a DAS, while explicitly factoring uncertainty into the process and resulting requirements. We introduce a variation of threat modeling to identify sources of uncertainty and demonstrate how the RELAX specification language can be used to specify more flexible requirements within a goal model to handle the uncertainty. © 2009 Springer Berlin Heidelberg.

Run-time resolution of uncertainty

Requirements awareness should help optimize requirements satisfaction when factors that were uncertain at design time are resolved at runtime. We use the notion of claims to model assumptions that cannot be verified with confidence at design time. By monitoring claims at runtime, their veracity can be tested. If falsified, the effect of claim negation can be propagated to the system's goal model and an alternative means of goal realization selected automatically, allowing the dynamic adaptation of the system to the prevailing environmental context. © 2011 IEEE.

RELAX:incorporating uncertainty into the specification of self-adaptive systems

Self-adaptive systems have the capability to autonomously modify their behaviour at run-time in response to changes in their environment. Self-adaptation is particularly necessary for applications that must run continuously, even under adverse conditions and changing requirements; sample domains include automotive systems, telecommunications, and environmental monitoring systems. While a few techniques have been developed to support the monitoring and analysis of requirements for adaptive systems, limited attention has been paid to the actual creation and specification of requirements of self-adaptive systems. As a result, self-adaptivity is often constructed in an ad-hoc manner. In this paper, we argue that a more rigorous treatment of requirements explicitly relating to self-adaptivity is needed and that, in particular, requirements languages for self-adaptive systems should include explicit constructs for specifying and dealing with the uncertainty inherent in self-adaptive systems. We present RELAX, a new requirements language for selfadaptive systems and illustrate it using examples from the smart home domain. © 2009 IEEE.

Describing and communicating uncertainty within the semantic web

The Semantic Web relies on carefully structured, well defined, data to allow machines to communicate and understand one another. In many domains (e.g. geospatial) the data being described contains some uncertainty, often due to incomplete knowledge; meaningful processing of this data requires these uncertainties to be carefully analysed and integrated into the process chain. Currently, within the SemanticWeb there is no standard mechanism for interoperable description and exchange of uncertain information, which renders the automated processing of such information implausible, particularly where error must be considered and captured as it propagates through a processing sequence. In particular we adopt a Bayesian perspective and focus on the case where the inputs / outputs are naturally treated as random variables. This paper discusses a solution to the problem in the form of the Uncertainty Markup Language (UncertML). UncertML is a conceptual model, realised as an XML schema, that allows uncertainty to be quantified in a variety of ways i.e. realisations, statistics and probability distributions. UncertML is based upon a soft-typed XML schema design that provides a generic framework from which any statistic or distribution may be created. Making extensive use of Geography Markup Language (GML) dictionaries, UncertML provides a collection of definitions for common uncertainty types. Containing both written descriptions and mathematical functions, encoded as MathML, the definitions within these dictionaries provide a robust mechanism for defining any statistic or distribution and can be easily extended. Universal Resource Identifiers (URIs) are used to introduce semantics to the soft-typed elements by linking to these dictionary definitions. The INTAMAP (INTeroperability and Automated MAPping) project provides a use case for UncertML. This paper demonstrates how observation errors can be quantified using UncertML and wrapped within an Observations & Measurements (O&M) Observation. The interpolation service uses the information within these observations to influence the prediction outcome. The output uncertainties may be encoded in a variety of UncertML types, e.g. a series of marginal Gaussian distributions, a set of statistics, such as the first three marginal moments, or a set of realisations from a Monte Carlo treatment. Quantifying and propagating uncertainty in this way allows such interpolation results to be consumed by other services. This could form part of a risk management chain or a decision support system, and ultimately paves the way for complex data processing chains in the Semantic Web.

Uncertainty analysis in the Model Web

This thesis provides a set of tools for managing uncertainty in Web-based models and workflows.To support the use of these tools, this thesis firstly provides a framework for exposing models through Web services. An introduction to uncertainty management, Web service interfaces,and workflow standards and technologies is given, with a particular focus on the geospatial domain.An existing specification for exposing geospatial models and processes, theWeb Processing Service (WPS), is critically reviewed. A processing service framework is presented as a solutionto usability issues with the WPS standard. The framework implements support for Simple ObjectAccess Protocol (SOAP), Web Service Description Language (WSDL) and JavaScript Object Notation (JSON), allowing models to be consumed by a variety of tools and software. Strategies for communicating with models from Web service interfaces are discussed, demonstrating the difficultly of exposing existing models on the Web. This thesis then reviews existing mechanisms for uncertainty management, with an emphasis on emulator methods for building efficient statistical surrogate models. A tool is developed to solve accessibility issues with such methods, by providing a Web-based user interface and backend to ease the process of building and integrating emulators. These tools, plus the processing service framework, are applied to a real case study as part of the UncertWeb project. The usability of the framework is proved with the implementation of aWeb-based workflow for predicting future crop yields in the UK, also demonstrating the abilities of the tools for emulator building and integration. Future directions for the development of the tools are discussed.

Living with uncertainty in the age of runtime models

Uncertainty can be defined as the difference between information that is represented in an executing system and the information that is both measurable and available about the system at a certain point in its life-time. A software system can be exposed to multiple sources of uncertainty produced by, for example, ambiguous requirements and unpredictable execution environments. A runtime model is a dynamic knowledge base that abstracts useful information about the system, its operational context and the extent to which the system meets its stakeholders' needs. A software system can successfully operate in multiple dynamic contexts by using runtime models that augment information available at design-time with information monitored at runtime. This chapter explores the role of runtime models as a means to cope with uncertainty. To this end, we introduce a well-suited terminology about models, runtime models and uncertainty and present a state-of-the-art summary on model-based techniques for addressing uncertainty both at development- and runtime. Using a case study about robot systems we discuss how current techniques and the MAPE-K loop can be used together to tackle uncertainty. Furthermore, we propose possible extensions of the MAPE-K loop architecture with runtime models to further handle uncertainty at runtime. The chapter concludes by identifying key challenges, and enabling technologies for using runtime models to address uncertainty, and also identifies closely related research communities that can foster ideas for resolving the challenges raised. © 2014 Springer International Publishing.

Risk and uncertainty in supply markets and sustainability-related innovation

tentially valuable innovations. In energy policy, much attention is given to analysing and incentivising customer demand, but new technologies also need new supply markets, to provide products and services to build, operate and maintain the innovative technology. This paper addresses the impact of supply constraints on the long-term viability of sustainability related innovations, using the case of energy from waste (EfW). Uncertainties in the pricing and availability of feedstock (i.e. waste) deter potential investors in EfW projects. We draw on prior supply management research to conceptualise the problem, and identify what steps might be taken to address it. Based on this analysis, we propose a research agenda aimed at purchasing and supply scholars and centred on the need to understand better how markets evolve and how stakeholders can (legitimately) influence the evolution of supply markets to support the adoption of sustainability related innovation. Within this broad case, specific themes are recommended for further investigation.

Fuzzy knowledge-based approach to treating uncertainty in inventory control

Inventory control in complex manufacturing environments encounters various sources of uncertainity and imprecision. This paper presents one fuzzy knowledge-based approach to solving the problem of order quantity determination, in the presence of uncertain demand, lead time and actual inventory level. Uncertain data are represented by fuzzy numbers, and vaguely defined relations between them are modeled by fuzzy if-then rules. The proposed representation and inference mechanism are verified using a large numbers of examples. The results of three representative cases are summarized. Finally a comparison between the developed fuzzy knowledge-based and traditional, probabilistic approaches is discussed.

Integrating OpenMI and Uncertweb:managing uncertainty in OpenMI models

OpenMI is a widely used standard allowing exchange of data between integrated models, which has mostly been applied to dynamic, deterministic models. Within the FP7 UncertWeb project we are developing mechanisms and tools to support the management of uncertainty in environmental models. In this paper we explore the integration of the UncertWeb framework with OpenMI, to assess the issues that arise when propagating uncertainty in OpenMI model compositions, and the degree of integration possible with UncertWeb tools. In particular we develop an uncertainty-enabled model for a simple Lotka-Volterra system with an interface conforming to the OpenMI standard, exploring uncertainty in the initial predator and prey levels, and the parameters of the model equations. We use the Elicitator tool developed within UncertWeb to identify the initial condition uncertainties, and show how these can be integrated, using UncertML, with simple Monte Carlo propagation mechanisms. The mediators we develop for OpenMI models are generic and produce standard Web services that expose the OpenMI models to a Web based framework. We discuss what further work is needed to allow a more complete system to be developed and show how this might be used practically.

Visualizing uncertainty in multi-spectral remotely sensed imagery

Error and uncertainty in remotely sensed data come from several sources, and can be increased or mitigated by the processing to which that data is subjected (e.g. resampling, atmospheric correction). Historically the effects of such uncertainty have only been considered overall and evaluated in a confusion matrix which becomes high-level meta-data, and so is commonly ignored. However, some of the sources of uncertainty can be explicity identified and modelled, and their effects (which often vary across space and time) visualized. Others can be considered overall, but their spatial effects can still be visualized. This process of visualization is of particular value for users who need to assess the importance of data uncertainty for their own practical applications. This paper describes a Java-based toolkit, which uses interactive and linked views to enable visualization of data uncertainty by a variety of means. This allows users to consider error and uncertainty as integral elements of image data, to be viewed and explored, rather than as labels or indices attached to the data. © 2002 Elsevier Science Ltd. All rights reserved.