发表机构
Aalborg University（奥尔堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对无线网络上的物理AI，提出了由线索信息量、可用性和时间可容许性共同决定的事件推理可靠性框架，定义了EIER与EIR，推导了事件错误下界并开发无线设计接口，通过室内活动推理实例验证了框架的有效性。
AI 中文摘要
支持无线通信的物理人工智能（physical AI）系统需要从可靠的数据传输转向可靠的物理事件推理。相关问题不仅是数据包是否到达，还包括决策节点处可用的线索集合（即证据）是否足够及时且具有信息量，以支持对事件的可靠推理。因此，本文开发了一个框架，其中事件推理可靠性（EIR）由线索信息量、线索可用性和时间可容许性共同决定，时间可容许性由下游任务需求决定，并通过有用性 horizon（有用性时限）表示。我们定义事件推理错误率（EIER）为纳入已接纳线索后归一化的剩余事件不确定性，EIR为对应的归一化不确定性降低，两者均以决策节点上下文为条件。我们进一步区分了证据受限的贝叶斯基准与特定推理引擎的运行性能，并从同一决策节点信息中推导了基于熵的最小可实现事件错误下界。该框架随后支持面向线索优先级、线索可靠性分配和事件推理覆盖特性的感知事件的无线设计接口。结合经验线索似然与无线传输的多类室内活动推理研究实例化了该框架，并展示了其如何在有限有用性时限下表征EIR。
英文摘要
Wireless-enabled physical artificial intelligence (physical AI) systems call for a shift from reliable data delivery to reliable inference of physical events. The relevant question is not only whether packets arrive, but whether the set of cues available at the decision node, i.e., the evidence, is sufficiently timely and informative to support reliable inference about the event. Accordingly, this paper develops a framework in which event-inference reliability (EIR) is determined jointly by cue informativeness, cue availability, and temporal admissibility. The latter is determined by the downstream task requirement and represented through the usefulness horizon. We define event-inference error ratio (EIER) as the normalised residual event uncertainty after incorporating admitted cues, and EIR as the corresponding normalised uncertainty reduction, both conditioned on decision-node context. We further distinguish the evidence-limited Bayes benchmark from operational performance of a particular inference engine and derive an entropy-based lower bound on the minimum achievable event error from the same decision-node information. The framework then enables an event-aware wireless design interface for cue prioritisation, cue-reliability allocation, and event-inference coverage characterisation. A multiclass indoor activity-inference study combining empirical cue likelihoods with wireless delivery instantiates the framework and demonstrates how it characterises EIR under finite usefulness horizons.
