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- Title
- Applying evolutionary computation techniques to address environmental uncertainty in dynamically adaptive systems
- Creator
- Ramirez, Andres J.
- Date
- 2013
- Collection
- Electronic Theses & Dissertations
- Description
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A dynamically adaptive system (DAS) observes itself and its execution environment at run time to detect conditions that warrant adaptation. If an adaptation is necessary, then a DAS changes its structure and/or behavior to continuously satisfy its requirements, even as its environment changes. It is challenging, however, to systematically and rigorously develop a DAS due to environmental uncertainty. In particular, it is often infeasible for a human to identify all possible combinations of...
Show moreA dynamically adaptive system (DAS) observes itself and its execution environment at run time to detect conditions that warrant adaptation. If an adaptation is necessary, then a DAS changes its structure and/or behavior to continuously satisfy its requirements, even as its environment changes. It is challenging, however, to systematically and rigorously develop a DAS due to environmental uncertainty. In particular, it is often infeasible for a human to identify all possible combinations of system and environmental conditions that a DAS might encounter throughout its lifetime. Nevertheless, a DAS must continuously satisfy its requirements despite the threat that this uncertainty poses to its adaptation capabilities. This dissertation proposes a model-based framework that supports the specification, monitoring, and dynamic reconfiguration of a DAS to explicitly address uncertainty. The proposed framework uses goal-oriented requirements models and evolutionary computation techniques to derive and fine-tune utility functions for requirements monitoring in a DAS, identify combinations of system and environmental conditions that adversely affect the behavior of a DAS, and generate adaptations on-demand to transition the DAS to a target system configuration while preserving system consistency. We demonstrate the capabilities of our model-based framework by applying it to an industrial case study involving a remote data mirroring network that efficiently distributes data even as network links fail and messages are dropped, corrupted, and delayed.
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- Title
- ASSURING THE ROBUSTNESS AND RESILIENCY OF LEARNING-ENABLED AUTONOMOUS SYSTEMS
- Creator
- Langford, Michael Austin
- Date
- 2022
- Collection
- Electronic Theses & Dissertations
- Description
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As Learning-Enabled Systems (LESs) have become more prevalent in safety-critical applications, addressing the assurance of LESs has become increasingly important. Because machine learning models in LESs are not explicitly programmed like traditional software, developers typically have less direct control over the inferences learned by LESs, relying instead on semantically valid and complete patterns to be extracted from the system’s exposure to the environment. As such, the behavior of an LES...
Show moreAs Learning-Enabled Systems (LESs) have become more prevalent in safety-critical applications, addressing the assurance of LESs has become increasingly important. Because machine learning models in LESs are not explicitly programmed like traditional software, developers typically have less direct control over the inferences learned by LESs, relying instead on semantically valid and complete patterns to be extracted from the system’s exposure to the environment. As such, the behavior of an LES is strongly dependent on the quality of its training experience. However, run-time environments are often noisy or not well-defined. Uncertainty in the behavior of an LES can arise when there is inadequate coverage of relevant training/test cases (e.g., corner cases). It is challenging to assure safety-critical LESs will perform as expected when exposed to run-time conditions that have never been experienced during training or validation. This doctoral research contributes automated methods to improve the robustness and resilience of an LES. For this work, a robust LES is less sensitive to noise in the environment, and a resilient LES is able to self-adapt to adverse run-time contexts in order to mitigate system failure. The proposed methods harness diversity-driven evolution-based methods, machine learning, and software assurance cases to train robust LESs, uncover robust system configurations, and foster resiliency through self-adaptation and predictive behavior modeling. This doctoral work demonstrates these capabilities by applying the proposed framework to deep learning and autonomous cyber-physical systems.
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- Title
- Harnessing evolutionary computation for the design and generation of adaptive embedded controllers within the context of uncertainty
- Creator
- Byers, Chad Michael
- Date
- 2015
- Collection
- Electronic Theses & Dissertations
- Description
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A critical challenge for the design of embedded controllers is incorporating desirable qualities such as robustness, fault tolerance, and adaptability into the control process in order to respond to dynamic environmental conditions. An embedded controller governs the execution of a task-specific system by monitoring information from its environment via sensors and producing an appropriate response through the system's actuators, often independent of any supervisory control. For a human...
Show moreA critical challenge for the design of embedded controllers is incorporating desirable qualities such as robustness, fault tolerance, and adaptability into the control process in order to respond to dynamic environmental conditions. An embedded controller governs the execution of a task-specific system by monitoring information from its environment via sensors and producing an appropriate response through the system's actuators, often independent of any supervisory control. For a human developer, identifying the set of all possible combinations of conditions a system might experience and designing a solution to accommodate this set is burdensome, costly, and often, infeasible. To alleviate this burden, a variety of techniques have been explored to automate the generation of embedded controller solutions. In this dissertation, we focus on the bio-inspired technique referred to as evolutionary computation where we harness evolution's power as a population-based, global search technique to build up good behavioral components. In this way, evolution naturally selects for these desirable qualities in order for a solution to remain competitive over time in the population. Often, these search techniques operate in the context of uncertainty where aspects of the (1) problem domain, (2) solution space, and (3) search process itself are subject to variation and change. To mitigate issues associated with uncertainty in the problem domain, we propose the digital enzyme, a biologically-inspired model that maps the complexity of both the environment and the system into the space of values rather than instructions. To address uncertainty in the solution space, we remove constraints in our initial digital enzyme model to allow the genome structure to be dynamic and open-ended, accommodating a wider range of evolved solution designs. Finally, to help explore the inherent uncertainty that exists in the search process, we uncover a hidden feature interaction present between the diversity-preserving search operator of a popular evolutionary algorithm and propose a new way to use niching as a means to mitigate its unwanted effects and bias on search.
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- Title
- Mitigating uncertainty at design time and run time to address assurance for dynamically adaptive systems
- Creator
- Fredericks, Erik M.
- Date
- 2015
- Collection
- Electronic Theses & Dissertations
- Description
-
A dynamically adaptive system (DAS) is a software system that monitors itself and its environment at run time to identify conditions that require self-reconfiguration to ensure that the DAS continually satisfies its requirements. Self-reconfiguration enables a DAS to change its configuration while executing to mitigate unexpected changes. While it is infeasible for an engineer to enumerate all possible conditions that a DAS may experience, the DAS must still deliver acceptable behavior in all...
Show moreA dynamically adaptive system (DAS) is a software system that monitors itself and its environment at run time to identify conditions that require self-reconfiguration to ensure that the DAS continually satisfies its requirements. Self-reconfiguration enables a DAS to change its configuration while executing to mitigate unexpected changes. While it is infeasible for an engineer to enumerate all possible conditions that a DAS may experience, the DAS must still deliver acceptable behavior in all situations. This dissertation introduces a suite of techniques that addresses assurance for a DAS in the face of both system and environmental uncertainty at different levels of abstraction. We first present a technique for automatically incorporating flexibility into system requirements for different configurations of environmental conditions. Second, we describe a technique for exploring the code-level impact of uncertainty on a DAS. Third, we discuss a run-time testing feedback loop to continually assess DAS behavior. Lastly, we present two techniques for introducing adaptation into run-time testing activities. We demonstrate these techniques with applications from two different domains: an intelligent robotic vacuuming system that must clean a room safely and efficiently and a remote data mirroring network that must efficiently and effectively disseminate data throughout the network. We also provide an end-to-end example demonstrating the effectiveness of each assurance technique as applied to the remote data mirroring application.
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- Title
- Using formal analysis and search-based techniques to address the assurance of cyber-physical systems at the requirements level
- Creator
- DeVries, Byron
- Date
- 2017
- Collection
- Electronic Theses & Dissertations
- Description
-
For high-assurance cyber-physical systems (CPS), such as the onboard features in modern transportation systems (e.g., automobiles, trains, and flight systems), ensuring acceptable and safe behavior is of paramount importance. Furthermore, the increasing complexity and the number of onboard features for autonomous vehicles further exacerbates the challenge of guaranteeing safe behavior. The operation of these high-assurance cyber-physical systems depends on the specification, implementation,...
Show moreFor high-assurance cyber-physical systems (CPS), such as the onboard features in modern transportation systems (e.g., automobiles, trains, and flight systems), ensuring acceptable and safe behavior is of paramount importance. Furthermore, the increasing complexity and the number of onboard features for autonomous vehicles further exacerbates the challenge of guaranteeing safe behavior. The operation of these high-assurance cyber-physical systems depends on the specification, implementation, and verification of those systems. Obstacles to assessing and ensuring assurance for cyber-physical system requirements may occur in many forms, but two significant sources of specification errors are incomplete requirements specifications and undesired feature interactions. In the case of incomplete requirements, it can be challenging to enumerate all the decomposed requirements necessary to satisfy a requirement (i.e., ensuring completeness), especially when considering different combinations of environmental conditions. A feature interaction occurs when two or more features satisfy specific properties in isolation, but no longer satisfy those properties when they are composed together. It may be necessary to analyze an exponential number of feature combinations to detect all possible interactions, resulting in a potentially exponential number of feature interaction results presented to the system developer. Furthermore, the uncertainty created by unexpected system and environmental scenarios exacerbates already difficult requirements specifications problems, many of which involve an exhaustive search for errors and their causes. That is, the exponential number of possibilities represents not only computational growth but also growth in the effort it takes the system designer to assess the results. This doctoral research tackles two key requirements assurance problems that exhibit these characteristics: requirements incompleteness and undesired feature interactions. The work explores how formal analysis and search-based techniques can be used in a complementary and synergistic fashion to address the assurance of cyber-physical systems facing environmental and system uncertainty, both at design time and run time. Industrial applications are used to demonstrate the respective techniques.
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