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Abstract

Machine semantic perception is discussed. One example system can comprise an environmental knowledgebase (KB) associating features with properties, and an interface component receiving sensor data associated with observed properties. The KB and sensor observations can be encoded in bit-matrix and bit-vector representations, respectively, for efficient storage and computation. A perception component can perform semantic perception on observed properties based on the KB. The perception component can determine explanatory features associated with the observed properties through abductive reasoning, and determine discriminatory properties associated with the explanatory features through deductive reasoning. These can be executed in an iterative and interleaved Perception Cycle for efficient computation of minimum actionable explanations of observations. The bit-matrix and bit-vector representations are presented for efficient computation of minimum actionable explanation using perception cycle, the iterative and interleaved application of hybrid abductive and deductive reasoning to seek contextually relevant discriminatory observations to systematically narrow explanatory features.

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