159,093 research outputs found
Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach
Goals are first-class entities in a self-adaptive system (SAS) as they guide
the self-adaptation. A SAS often operates in dynamic and partially unknown
environments, which cause uncertainty that the SAS has to address to achieve
its goals. Moreover, besides the environment, other classes of uncertainty have
been identified. However, these various classes and their sources are not
systematically addressed by current approaches throughout the life cycle of the
SAS. In general, uncertainty typically makes the assurance provision of SAS
goals exclusively at design time not viable. This calls for an assurance
process that spans the whole life cycle of the SAS. In this work, we propose a
goal-oriented assurance process that supports taming different sources (within
different classes) of uncertainty from defining the goals at design time to
performing self-adaptation at runtime. Based on a goal model augmented with
uncertainty annotations, we automatically generate parametric symbolic formulae
with parameterized uncertainties at design time using symbolic model checking.
These formulae and the goal model guide the synthesis of adaptation policies by
engineers. At runtime, the generated formulae are evaluated to resolve the
uncertainty and to steer the self-adaptation using the policies. In this paper,
we focus on reliability and cost properties, for which we evaluate our approach
on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the
validation are promising and show that our approach is able to systematically
tame multiple classes of uncertainty, and that it is effective and efficient in
providing assurances for the goals of self-adaptive systems
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Restructuring Controllers to Accommodate Plant Nonlinearities
This thesis explores the possibility of controller restructuring for improved closed-loop performance of nonlinear plants using a gradient based method of symbolic adaptation- Model Structure Adaptation Method (MSAM). The adaptation method starts with a controller which is a linear controller designed according to the linearized model of the nonlinear plant. This controller is then restructured into a series of nonlinear candidate controllers and adapted iteratively toward a desired closed-loop response. The noted feature of the adaptation method is its ability to quantify structural perturbations to the controllers. This quantification is important in scaling the structural Jacobian that is used in gradient-based adaptation of the candidate controllers. To investigate this, two nonlinear plants with unknown nonlinearities viz., nonlinear valve and nonlinear inverted pendulum are chosen. Furthermore, the properties of restructured controllers obtained for two systems, stability, effect of measurement noise, reachability, scalability and algorithmic issues of MSAM are studied and compared with the starting controller
Combining case based reasoning with neural networks
This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and
CBR are instance-based learning techniques, although neural nets work with numerical data and CBR systems work with symbolic data. This paper discusses how the application scope of both paradigms could be enhanced by the use of hybrid concepts. To make the use of neural networks possible, the problem's situation and solution features are transformed into continuous features, using techniques similar to CBR's definition of similarity metrics. Radial Basis Function (RBF) neural nets are used to create a multivariable, continuous input-output mapping. As the mapping is continuous, this technique also provides generalisation between cases, replacing the domain specific
solution adaptation techniques required by conventional CBR. This continuous representation also allows, as in
fuzzy logic, an associated membership measure to be output with each symbolic feature, aiding the prioritisation of various possible solutions. A further advantage is that, as the RBF neurons are only active in a limited area of the input space, the solution can be accompanied by local estimates of accuracy, based on the sufficiency of the cases present in that area as well as the results measured during testing. We describe how the application of this technique could be of benefit to the real world problem of sales advisory systems, among others
Combining case based reasoning with neural networks
This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and
CBR are instance-based learning techniques, although neural nets work with numerical data and CBR systems work with symbolic data. This paper discusses how the application scope of both paradigms could be enhanced by the use of hybrid concepts. To make the use of neural networks possible, the problem's situation and solution features are transformed into continuous features, using techniques similar to CBR's definition of similarity metrics. Radial Basis Function (RBF) neural nets are used to create a multivariable, continuous input-output mapping. As the mapping is continuous, this technique also provides generalisation between cases, replacing the domain specific
solution adaptation techniques required by conventional CBR. This continuous representation also allows, as in
fuzzy logic, an associated membership measure to be output with each symbolic feature, aiding the prioritisation of various possible solutions. A further advantage is that, as the RBF neurons are only active in a limited area of the input space, the solution can be accompanied by local estimates of accuracy, based on the sufficiency of the cases present in that area as well as the results measured during testing. We describe how the application of this technique could be of benefit to the real world problem of sales advisory systems, among others
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