Continuous Cognitive Coverage for Autonomous Robots via Event-Dependent Cognitive Treatment and Learning

2026-09-04

AI Summary

本文提出一种持续认知覆盖框架，通过事件依赖的认知处理和学习方法，解决自主机器人对所有认知事件进行适当处理的问题。

Abstract

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.

Problem

• autonomous robots
• continuous cognitive coverage
• event-dependent treatment
• cognitive processing
• differentiated treatment

Innovation

• Continuous Cognitive Coverage
• Event-Dependent Treatment
• Autonomous Robots
• Cognitive Processing
• Continual Learning