As automatic optimization techniques find their way into industrial applications, the behavior of many complex systems is determined by some form of planner picking the right actions to optimize a given objective function. In many cases, the mapping of plans to objective reward may change due to unforeseen events or circumstances in the real world. In those cases, the planner usually needs some additional effort to adjust to the changed situation and reach its previous level of performance. Whenever we still need to continue polling the planner even during re-planning, it oftentimes exhibits severely lacking performance. In order to improve the planner's resilience to unforeseen change, we argue that maintaining a certain level of diversity amongst the considered plans at all times should be added to the planner's objective. Effectively, we encourage the planner to keep alternative plans to its currently best solution. As an example case, we implement a diversity-aware genetic algorithm using two different metrics for diversity (differing in their generality) and show that the blow in performance due to unexpected change can be severely lessened in the average case. We also analyze the parameter settings necessary for these techniques in order to gain an intuition how they can be incorporated into larger frameworks or process models for software and systems engineering.
The preparation for unexpected or previously wrongly modeled change is an important issue for the practical application of machine learning in industry @cite_22 . From an engineer's point of view, the diversity of the population of plans can be regarded as a typical with the cost of the plan representing the functional requirement. Applying NFR engineering processes to self-adaptive systems is still a new idea and a clear canon of relevant NFRs for these new challenges has not yet been found @cite_29 @cite_43 .
@cite_43 @cite_29 @cite_22
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Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics applications exhibit these conditions and thus facilitate the potential to incrementally adapt a learnt model over minor shifts which integrate to massive differences over time. Our work presents an adversarial approach for lifelong, incremental domain adaptation which benefits from unsupervised alignment to a series of intermediate domains which successively diverge from the labelled source domain. We empirically demonstrate that our incremental approach improves handling of large appearance changes, e.g. day to night, on a traversable-path segmentation task compared with a direct, single alignment step approach. Furthermore, by approximating the feature distribution for the source domain with a generative adversarial network, the deployment module can be rendered fully independent of retaining potentially large amounts of the related source training data for only a minor reduction in performance. The rapid advance in information processing systems in recent decades had directed engineering research towards the development of intelligent systems that can evolve models of natural phenomena automatically—“by themselves,” so to speak. In this respect, a wide range of machine learning techniques like decision trees, artificial neural networks (ANNs), Bayesian methods, fuzzy-rule based systems, and evolutionary algorithms have been successfully applied to model different civil engineering systems. In this study, the possibility of using yet another machine learning paradigm that is firmly based on the theory of statistical learning, namely that of the support vector machine (SVM), is investigated. An interesting property of this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over the data set. In this paper, the basic ... Modern, complex software systems (e-commerce, IT, critical infrastructures, etc.) are increasingly required to continue operation in the face of change, to self-adapt to accommodate shifting user priorities, resource variability, changing environments, and component failures. While manual oversight benefits from global problem contexts and flexible policies, human operators are costly and prone to error. Low-level, embedded mechanisms (exceptions, time-outs, etc.) are effective and timely for error recovery, but are local in scope to the point-of-failure, application-specific, and costly to modify when adaptation objectives change. An ideal solution leverages domain expertise, provides an end-to-end system perspective, adapts the system in a timely manner, and can be engineered cost-effectively. Architecture-based self-adaptation closes the “loop of control,” using external mechanisms and the architecture model of the target system to adapt the system. An architecture model exposes important system properties and constraints, provides end-to-end problem contexts, and allows principled and automated adaptations. Existing architecture-based approaches specialize support for particular classes of systems and fixed sets of quality-of-service concerns; they are costly to develop for new systems and to evolve for new qualities. To overcome these limitations, we posit this thesis: We can provide software engineers the ability to add and evolve self-adaptation capabilities cost-effectively, for a wide range of software systems, and for multiple objectives, by defining a self-adaptation framework that factors out common adaptation mechanisms and provides explicit customization points to tailor self-adaptation capabilities for particular classes of systems, for multiple quality-of-service objectives. Our approach, embodied in a system called Rainbow, provides an engineering approach and a framework of mechanisms to monitor a target system and its environment, reflect observations into the system's architecture model, detect opportunities for improvements, select a course of action, and effect changes. The framework provides general and reusable infrastructures with well-defined customization points, a set of abstractions, and an adaptation engineering process, focusing engineers on adaptation concerns to systematically customize Rainbow to particular systems. To automate system self-adaptation, Rainbow provides a language, called Stitch, to represent routine human adaptation knowledge using a core set of adaptation concepts. In this paper, we make two contributions to unsupervised domain adaptation (UDA) using the convolutional neural network (CNN). First, our approach transfers knowledge in all the convolutional layers through attention alignment. Most previous methods align high-level representations, e.g., activations of the fully connected (FC) layers. In these methods, however, the convolutional layers which underpin critical low-level domain knowledge cannot be updated directly towards reducing domain discrepancy. Specifically, we assume that the discriminative regions in an image are relatively invariant to image style changes. Based on this assumption, we propose an attention alignment scheme on all the target convolutional layers to uncover the knowledge shared by the source domain. Second, we estimate the posterior label distribution of the unlabeled data for target network training. Previous methods, which iteratively update the pseudo labels by the target network and refine the target network by the updated pseudo labels, are vulnerable to label estimation errors. Instead, our approach uses category distribution to calculate the cross-entropy loss for training, thereby ameliorating the error accumulation of the estimated labels. The two contributions allow our approach to outperform the state-of-the-art methods by +2.6 on the Office-31 dataset.