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论文类型：会议论文

发表时间：2021-04-18

关键字：Fusion ART; FALCON; Information Intensity Attenuation; Node Pruning; Minefield Navigation Task

摘要：Fusion ART is an enhanced version of Adaptive Resonance Theory (ART) which is derived from a biologically-plausible theory of human cognitive information processing. Due to its well-established ability of learning associative mappings across multimodal pattern channels in an online and incremental manner, fusion ART has been widely applied in many real world learning problems. In this paper, we take a Fusion Architecture for Learning, Cognition, and Navigation (FALCON) as the specification and essential backbone of fusion ART and introduce an intensity attenuation controller delta for adaptively adjusting the intensity of information captured from the environment, by taking inspiration from Broadbent-Treisman Filter-Attenuation's perceptual model of environmental attention. Particularly, we propose both an adaptive delta detection algorithm as well as a delta-based pruning algorithm to enhance the learning performance of FALCON while reduce the redundant memory storage incurred by the "detrimental delta". To verify the effectiveness and efficiency of our proposed method, comprehensive experimental studies are carried out on a classical minefield navigation task.