From Continuous Observations to Symbolic Concepts: A Discrimination-Based Strategy for Grounded Concept Learning.

ABSTRACT: Autonomous agents perceive the world through streams of continuous sensori-motor data. Yet, in order to reason and communicate about their environment, agents need to be able to distill meaningful concepts from their raw observations. Most current approaches that bridge between the continuous and symbolic domain are using deep learning techniques. While these approaches often achieve high levels of accuracy, they rely on large amounts of training data, and the resulting models lack transparency, generality, and adaptivity. In this paper, we introduce a novel methodology for grounded concept learning. In a tutor-learner scenario, the method allows an agent to construct a conceptual system in which meaningful concepts are formed by discriminative combinations of prototypical values on human-

SUBMITTER: Nevens J 

PROVIDER: S-EPMC7806012 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature