https://home.mis.u-picardie.fr/~fabio/Eng/documenti/articoli/CoMoPrTo_AUTO08.pdf

Brief paper: Leader-follower formation control of nonholonomic mobile robots with input constraints

Fundamentals Of Neural Networks Architectures Algorithms And Applications

Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (e.g., adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems. This book is one of the outcomes of the Dagstuhl Seminar 11481 on models@run.time held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.

Models@run.time: foundations, applications, and roadmaps

Deep learning has become the method of choice to tackle real-world problems in different domains, partly because of its ability to learn from data and achieve impressive performance on a wide range of applications. However, its success usually relies on two assumptions: (i) vast troves of labeled datasets are required for accurate model fitting, and (ii) training and testing data are independent and identically distributed. Its performance on unseen target domains, thus, is not guaranteed, especially when encountering out-of-distribution data at the adaptation stage. The performance drop on data in a target domain is a critical problem in deploying deep neural networks that are successfully trained on data in a source domain. Unsupervised domain adaptation (UDA) is proposed to counter this, by leveraging both labeled source domain data and unlabeled target domain data to carry out various tasks in the target domain. UDA has yielded promising results on natural image processing, video analysis, natural language processing, time-series data analysis, medical image analysis, etc. In this review, as a rapidly evolving topic, we provide a systematic comparison of its methods and applications. In addition, the connection of UDA with its closely related tasks, e.g., domain generalization and out-of-distribution detection, has also been discussed. Furthermore, deficiencies in current methods and possible promising directions are highlighted.

Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives

More than a decade ago, the research topic models@run.time was coined. Since then, the research area has received increasing attention. Given the prolific results during these years, the current outcomes need to be sorted and classified. Furthermore, many gaps need to be categorized in order to further develop the research topic by experts of the research area but also newcomers. Accordingly, the paper discusses the principles and requirements of models@run.time and the state of the art of the research line. To make the discussion more concrete, a taxonomy is defined and used to compare the main approaches and research outcomes in the area during the last decade and including ancestor research initiatives. We identified and classified 275 papers on models@run.time, which allowed us to identify the underlying research gaps and to elaborate on the corresponding research challenges. Finally, we also facilitate sustainability of the survey over time by offering tool support to add, correct and visualize data.

Models@run.time: a Guided Tour of the State-of-the-Art and Research Challenges

Self-adaptive systems (SAS) may be subjected to conditions for which they were not explicitly designed. For those high-assurance SAS applications that must deliver critical services, techniques are needed to ensure that only acceptable behavior is provided. While testing an SAS at design time can validate its expected behaviors in known circumstances, testing at run time provides assurance that the SAS will continue to behave as expected in uncertain situations. This paper introduces Veritas, an approach for using utility functions to guide the test adaptation process as part of a run-time testing framework. Specifically, Veritas adapts test cases for an SAS at run time to ensure that the SAS continues to execute in a safe and correct manner when adapting to handle changing environmental conditions.

Towards run-time adaptation of test cases for self-adaptive systems in the face of uncertainty

The validity of systems at run time depends on the features included in those systems operating as specified However, when feature interactions occur, the specifications no longer reflect the state of the run-time system due to the conflict While methods exist to detect feature interactions at design time, conflicts that cause features to fail may still arise when new detected feature interactions are considered unreachable, new features are added, or an exhaustive design-time detection approach is impractical due to computational costs This paper introduces Thoosa, an approach for using models at run time to detect features that can fail due to n-way feature interactions at run time and thereby trigger mitigating adaptations and/or updates to the requirements We illustrate our approach by applying Thoosa to an industry-based automotive braking system comprising multiple subsystems

https://dl.acm.org/doi/pdf/10.1145/3194133.3194141

Run-time monitoring of self-adaptive systems to detect N-way feature interactions and their causes

The usefulness of a system specification depends in part on the completeness of the requirements. However, enumerating all necessary requirements is difficult, especially when requirements interact with an unpredictable environment. A specification built with an idealized environmental view is incomplete if it does not include requirements to handle non-idealized behavior. Often incomplete requirements are not detected until implementation, testing, or worse, after deployment. Even when performed during requirements analysis, detecting incomplete requirements is typically an error prone, tedious, and manual task. This paper introduces Ares, a design-time approach for detecting incomplete requirements decomposition using symbolic analysis of hierarchical requirements models. We illustrate our approach by applying Ares to a requirements model of an industry-based automotive adaptive cruise control system. Ares is able to automatically detect specific instances of incomplete requirements decompositions at design-time, many of which are subtle and would be difficult to detect, either manually or with testing.

https://dl.acm.org/doi/pdf/10.1145/2976767.2976791

Automatic detection of incomplete requirements via symbolic analysis

Adaptive controllers enable cyber-physical systems, such as autonomous robots, to manage uncertain conditions during execution. However, there is a limit to the range of conditions that can be handled by a given controller. When this limit is exceeded, a controller might fail to respond as expected, not only rendering it ineffective but possibly putting the entire system at risk. In this paper, we describe a method based on evolutionary search for automatically enhancing, and discovering the boundaries of, a given adaptive controller. Collectively, these boundaries define an execution mode for that controller. Explicit specification of mode boundaries facilitates the development of decision logic that determines, based on system state and sensed conditions, when to switch to a different execution mode and typically a different controller, such as one for providing fail-safe operation. To evaluate the proposed approach, we apply it to a robotic fish propelled by a flexible caudal fin that is governed by a model-free adaptive controller. Experimental results demonstrate that this approach is effective in characterizing a controller's ability to adapt to environmental dynamics, including physical damage to the robot itself.

An evolutionary approach to discovering execution mode boundaries for adaptive controllers

Ensuring acceptable and safe behavior is paramount for high-assurance systems. However, independently-developed features often exhibit overlapping, yet conflicting behavior termed feature interactions. This paper introduces Phorcys, a design-time approach for detecting unwanted failures caused by n-way feature interactions at the requirements level using both symbolic analysis and evolutionary computation. Unlike previous n-way feature interaction detection approaches that look for each unique unwanted interactions, Phorcys analyzes each feature for its ability to cause unwanted behavior, including failures. By using a combination of symbolic analysis and evolutionary computation, Phorcys is able to identify multiple counterexamples, thus providing more guidance for mitigation (e.g., revising specifications, adding constraints, etc.). To the best of the authors' knowledge, Phorcys is the only technique to detect failures caused by n-way feature interactions using a combination of symbolic analysis and evolutionary computation. We illustrate our approach by applying Phorcys to an industry-based automotive braking system comprising multiple subsystems.

Automatic Detection of Feature Interactions Using Symbolic Analysis and Evolutionary Computation

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