Solving the bongard-logo problem by modeling a probabilistic model

arXiv:2403.03173v1 Announce Type: new
Abstract: Abstract reasoning problems challenge the perceptual and cognitive abilities of AI algorithms, demanding deeper pattern discernment and inductive reasoning beyond explicit image features. This study introduces PMoC, a tailored probability model for the Bongard-Logo problem, achieving high reasoning accuracy by constructing independent probability models. Additionally, we present Pose-Transformer, an enhanced Transformer-Encoder designed for complex abstract reasoning tasks, including Bongard-Logo, RAVEN, I-RAVEN, and PGM. Pose-Transformer incorporates positional information learning, inspired by capsule networks' pose matrices, enhancing …

abstract accuracy ai algorithms algorithms arxiv beyond challenge cognitive cs.cv features image independent inductive logo modeling probabilistic model probability reasoning study type