Auto-COP：使用强化学习选项的上下文导向编程中的适应生成
Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options
AI总结：
Auto-COP利用强化学习选项在运行时生成COP适应，解决复杂CPS中设计时定义适应的难题，通过驾驶助手和机器人配送案例验证其正确性并使执行步骤减半。
摘要：
自适应软件系统持续适应其执行环境中的内部和外部变化，这些变化被捕获为上下文。COP范式提出了一种开发自适应系统的技术，通过专门的编程语言构造捕获其主要特征。COP适应被指定为独立模块，随着上下文响应来自周围环境的感知情况而被激活和停用，这些模块在基础系统中组合进出。然而，适应的定义、其上下文及相关联的专门行为需要在设计时指定。在复杂的CPS中，由于新的不可预测的操作条件，这是难以实现的。我们提出了Auto-COP，一种在运行时实现适应生成的新技术。Auto-COP使用RL选项基于系统执行的先前实例来构建动作序列。选项在与环境的交互中进行探索，并且每个上下文最合适的选项被用于生成利用COP的适应。为了验证Auto-COP，我们提出了两个展示不同系统特征和应用领域的案例研究：驾驶助手和机器人交付系统。我们展示了在运行时生成的Auto-COP代码示例，以说明需要适应的情况（上下文）类型，以及每个上下文对应的生成适应。我们确认，生成的适应表现出由领域特定性能指标衡量的正确系统行为，同时将所需的执行/驱动步骤数减少了一半，表明当自适应行为比原始动作的执行更合适时，适应被运行系统定期选择。
英文摘要：
Self-adaptive software systems continuously adapt in response to internal and external changes in their execution environment, captured as contexts. The COP paradigm posits a technique for the development of self-adaptive systems, capturing their main characteristics with specialized programming language constructs. COP adaptations are specified as independent modules composed in and out of the base system as contexts are activated and deactivated in response to sensed circumstances from the surrounding environment. However, the definition of adaptations, their contexts and associated specialized behavior, need to be specified at design time. In complex CPS this is intractable due to new unpredicted operating conditions. We propose Auto-COP, a new technique to enable generation of adaptations at run time. Auto-COP uses RL options to build action sequences, based on the previous instances of the system execution. Options are explored in interaction with the environment, and the most suitable options for each context are used to generate adaptations exploiting COP. To validate Auto-COP, we present two case studies exhibiting different system characteristics and application domains: a driving assistant and a robot delivery system. We present examples of Auto-COP code generated at run time, to illustrate the types of circumstances (contexts) requiring adaptation, and the corresponding generated adaptations for each context. We confirm that the generated adaptations exhibit correct system behavior measured by domain-specific performance metrics, while reducing the number of required execution/actuation steps by a factor of two showing that the adaptations are regularly selected by the running system as adaptive behavior is more appropriate than the execution of primitive actions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
