Faster Unsupervised Object Detection For Symbolic Representation

Abstract

Symbolic artificial intelligence has seen a wave of intense research in the late 20th century. More recently, the field of deep learning and deep reinforcement learning has been making large strides in terms of computer vision and robotic applications. Both fields have impressive accomplishments but are situated on two opposite ends of the spectrum in AI research. Mainstream deep learning relies on automatic feature extraction which often includes abstract features while symbolic AI often relies on handcrafting symbols and semantics. In this work, we introduce a deep learning algorithm for learning symbolic representation. The algorithm is bases on recent advances in un-supervised object detection, and we demonstrate that it ca be easily adapted for symbolic representation. Our algorithm, FaSPAIR, is an adaption of the object detection algorithm SPAIR. We have made several changes to bridge the model to the symbolic representation needed for reinforcement learning and to improve training speed. Our results demonstrate the efficacy of using object detection for learning symbolic representation. We also demonstrate that FaSPAIR has a large boost in computation speed compared to the current state of the art algorithm SPAIR.