Knowledge management
Related papers: 20
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Knowledge management in robotics and AI refers to the systematic processes by which intelligent systems acquire, organize, store, retrieve, and apply information to support decision-making and task execution. It encompasses techniques for representing knowledge — such as ontologies, knowledge graphs, and rule-based systems — as well as methods for updating and sharing that knowledge across agents, systems, or organizations. In robotics, knowledge management enables robots to draw on structured information about their environment, tasks, and interactions to behave intelligently in dynamic settings. In broader AI applications, it supports systems like chatbots, recommendation engines, and decision-support tools that must integrate diverse data sources and contextual understanding. Knowledge management becomes especially critical in collaborative multi-agent systems, human-robot interaction, and Industry 4.0 environments, where accurate, accessible, and current knowledge underpins coordination and efficiency. As AI systems grow more autonomous and are deployed across sectors such as manufacturing, agriculture, and services, robust knowledge management ensures these systems remain adaptable, explainable, and aligned with human goals — making it a foundational capability for trustworthy and scalable AI deployment.
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