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[1N5-OS-10b-04] Reducing the Cost of Learning Places via a Model that Integrates Probabilistic Logic and Spatial Concept
Keywords:Integration of System1 and System2, Probabilistic Logic, Probabilistic Generative Model, Logical Inference, Service Robot
We propose a method that integrates probabilistic logic and spatial concept to enable a robot to acquire knowledge of the relationships between objects and places in a new environment with a few learning times. By combining logical inference with prior knowledge and cross-modal inference within spatial concept, the robot can infer the place of an object even when the probability of its existence is a priori unknown. We conducted experiments in which a robot searched for objects in a simulation environment using four methods: 1) spatial concept only, 2) prior knowledge only, 3) spatial concept and prior knowledge, and 4) probabilistic logic and spatial concept (proposed). We confirmed the effectiveness of the proposed method by comparing the number of place visits it took for the robot to find all the objects. We observed that the robot could find the objects faster using the proposed method.
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