发表机构
School of Computer Science, Chengdu University of Information Technology（成都信息工程大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种自主机器人连续认知覆盖框架，按事件状态等分配差异化认知处理，经实验验证其在结构化处理准确率等指标上表现良好，陌生事件复用可实现100%自动处理。
AI 中文摘要
自主机器人会持续遇到物体、变化和情况，所有被纳入认知的事件都应得到适当的认知处理，而非一直未被处理直到明确任务需要关注。然而，现有的任务驱动、反应式或固定推理方法通常仅处理选定事件或应用预定义推理流程，难以提供带差异化处理的连续认知覆盖。本文提出一种连续认知覆盖框架，其中每个被认知接纳的事件会根据其状态、上下文和历史被分配事件依赖的认知处理。不同事件因此可调用描述、记忆、风险预测、规划、诊断、类比或其他学习到的处理方式。熟悉的事件可通过学习到的机制自动处理，而陌生或不确定的事件则调用明确的审议或 fallback 推理。多个认知过程可被暂停、恢复和交错，以便在新事件到达或现有事件等待证据时，认知处理能持续进行。经验证的经验会被持续学习，以自动化、优化和修正事件特定的处理方式。实验取得了96.76%的结构化处理准确率、93.66%的自动处理率、在突发延迟工作负载下92.64%的认知覆盖率，以及79.53%的持续学习联合准确率，陌生事件复用达到100%自动处理率。
英文摘要
Autonomous robots continuously encounter objects, changes, and situations, and every event admitted into cognition should receive an appropriate cognitive treatment rather than remain untreated until an explicit task requires attention. However, existing task-driven, reactive, or fixed-reasoning approaches generally process only selected events or apply predefined reasoning procedures, making it difficult to provide continuous cognitive coverage with differentiated treatment. This paper proposes a continuous cognitive coverage framework in which every cognitively admitted event is assigned an event-dependent cognitive treatment according to its state, context, and history. Different events may therefore invoke description, memory, risk prediction, planning, diagnosis, analogy, or other learned treatments. Familiar events can be processed automatically by learned mechanisms, whereas unfamiliar or uncertain events invoke explicit deliberation or fallback reasoning. Multiple cognitive processes can be suspended, resumed, and interleaved so that cognitive processing continues as new events arrive or existing events await evidence. Validated experiences are continuously learned to automate, refine, and revise event-specific treatments. Experiments achieve 96.76% structured treatment accuracy with 93.66% automatic processing, 92.64% cognitive coverage under bursty-delayed workloads, and 79.53% continual-learning joint accuracy, with novel-event reuse reaching 100% automatic processing.
