Description logic
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Description logic (DL) is a family of formal knowledge representation languages used to define structured ontologies — hierarchical vocabularies of concepts, roles, and individuals — and to reason automatically over their logical consequences. Rooted in first-order logic but deliberately restricted to ensure computational tractability, DLs underpin standards such as OWL (Web Ontology Language), which has become the backbone of semantic knowledge bases across AI and robotics. In robotics and AI, description logic enables robots to build and query rich semantic models of their environments, linking perceptual data to symbolic knowledge. Applications include context-aware smart environments, semantic mapping, object classification, and human-robot collaboration, where a robot can infer relationships between objects, locations, and tasks that were never explicitly programmed. DL-based ontologies can also be combined with probabilistic and temporal extensions to handle real-world uncertainty and dynamic scenes. Description logic matters because it provides a principled, machine-checkable foundation for robot reasoning that is both expressive enough to capture complex domain knowledge and constrained enough to guarantee that inference terminates reliably — bridging the gap between raw sensor data and high-level symbolic decision-making.
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Temporal Logic Inference with Prior Information: An Application to Robot Arm Movements**Corresponding Author: Zhe Xu. We acknowledge the support of the National Science Foundation through grants number CNS0953976, CNS-1218109, and NRI-1426907, and the O_ce of Naval Research through grant number N00014-14-1-0554 for the research reported in this paper.
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