主动式计算
Proactive Computing
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中文总结 AI 辅助
本综述定义主动式计算范式，区分其与相关计算类型，梳理技术使能因素与挑战，指出关键研究挑战为确定系统代表用户行动的时机、方式与可行性。
中文摘要 AI 辅助
计算系统正从反应式工具向在用户明确请求前进行感知、解读、预测与行动的系统转变，这一转变由移动连接的全球规模、可穿戴与环境感知的快速扩展、机器学习与基础模型的进步、分布式边缘基础设施及物理执行器所推动。我们将主动式计算定义为一种范式，其中系统推断用户情境、预测未来需求或风险，并在适当时机发起信息交付或行动。本综述将主动式计算与反应式、情境感知式、自适应式及预测式计算区分开来，将主动性构建为跨感知、理解、决策、行动与治理的系统级集成问题。我们回顾了主动式计算的技术使能因素，梳理了其设计空间，分析了不确定性感知触发、预测到行动的差距等技术挑战，并讨论了涉及用户接受度、信任、隐私、问责制、公平性及可持续性的社会技术问题。我们认为，关键研究挑战不仅是提升预测准确率，还包括确定系统应代表用户何时、如何及是否采取行动。
英文摘要
Computing systems are moving from reactive tools toward systems that sense, interpret, predict, and act before explicit user requests. This transition is enabled by the global scale of mobile connectivity, the rapid expansion of wearable and ambient sensing, advances in machine learning and foundation models, distributed edge infrastructure, and physical actuation. We define \emph{proactive computing} as a paradigm in which systems infer user context, anticipate future needs or risks, and initiate information delivery or actions at an appropriate time. This survey distinguishes proactive computing from reactive, context-aware, adaptive, and predictive computing, and frames proactivity as a system-level integration problem across sensing, understanding, decision making, action, and governance. We review the technological enablers of proactive computing, organize its design space, analyze technical challenges such as uncertainty-aware triggering and the prediction-to-action gap, and discuss socio-technical issues involving user acceptance, trust, privacy, accountability, fairness, and sustainability. We argue that the key research challenge is not merely improving prediction accuracy, but determining when, how, and whether systems should act on behalf of users.
