Senseknowledge

Senseknowledge is a term used in AI and cognitive science to denote knowledge that is grounded in perceptual sensing. It encompasses information, interpretations, and inferred models derived directly from sensory input, such as vision, audition, and touch, and is often contrasted with abstract, symbolic, or encyclopedic knowledge that is independent of current perception.

Acquisition and processing involve sensor data collection, sensor fusion, perception models, and real-time inference. Methods include multimodal integration, probabilistic reasoning under uncertainty, and active sensing to seek informative inputs. Senseknowledge can be learned from data or elicited through interaction with environments.

Relation to other concepts: it complements symbolic knowledge and causal models; some scholars view senseknowledge as a bridge enabling more robust AI by grounding representations in real-world perception. Criticisms include definitional vagueness and the risk of conflating sensory data with knowledge.

Applications: autonomous robotics, human-robot interaction, augmented reality, and situational awareness systems rely on senseknowledge to interpret environments, reason about states, and plan actions.

The term is informal and not universally standardized; usage varies by field. Related topics include sensor fusion, perception, embodied cognition, and knowledge grounding.