Tractable Boolean and Arithmetic Circuits
This is an incremental review article summarizing existing research on circuits for neuro-symbolic AI, without presenting new results.
The paper reviews tractable Boolean and arithmetic circuits, which were developed to facilitate logical and probabilistic reasoning by enabling linear-time inference, and have expanded to serve as a backbone for integrating knowledge, reasoning, and learning in neuro-symbolic AI.
Tractable Boolean and arithmetic circuits have been studied extensively in AI for over two decades now. These circuits were initially proposed as "compiled objects," meant to facilitate logical and probabilistic reasoning, as they permit various types of inference to be performed in linear-time and a feed-forward fashion like neural networks. In more recent years, the role of tractable circuits has significantly expanded as they became a computational and semantical backbone for some approaches that aim to integrate knowledge, reasoning and learning. In this article, we review the foundations of tractable circuits and some associated milestones, while focusing on their core properties and techniques that make them particularly useful for the broad aims of neuro-symbolic AI.