Neural Production Systems

By Anirudh Goyal, Aniket Rajiv Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Manfred Otto Heess, Michael C. Mozer, Yoshua Bengio. 2021

94 citations recorded by the source corpus.

Visual environments are structured, consisting of distinct objects or entities. These entities have properties -- both visible and latent -- that determine the manner in which they interact with one another. To partition images into entities, deep-learning researchers have proposed structural inductive biases such as slot-based architectures. To model interactions among entities, equivariant graph neural nets (GNNs) are used, but these are not particularly well suited to the task for two reasons. First, GNNs do not predispose interactions to be sparse, as relationships among independent entities are likely to be. Second, GNNs do not factorize knowledge about interactions in an entity-conditional manner. As an alternative, we take inspiration from cognitive science and resurrect a classic approach, production systems, which consist of a set of rule templates that are applied by binding placeholder variables in the rules to specific entities. Rules are scored on their match to entities, and the best fitting rules are applied to update entity properties. In a series of experiments, we demonstrate that this architecture achieves a flexible, dynamic flow of control and serves to factorize entity-specific and rule-based information. This disentangling of knowledge achieves robust future-state prediction in rich visual environments, outperforming state-of-the-art methods using GNNs, and allows for the extrapolation from simple (few object) environments to more complex environments.

Subfields: Model Architectures

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Model Architectures

Citations in the corpus

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA (2021, background): “The hypothesis that high-level concepts can be described by a sparse dependency graph has been described and leveraged for out-of-distribution generalization originally by Bengio (2019) and Goyal et al. (2021b), which were early sources of inspiration for this work.”
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning (2021, methodology): “Other approaches for solving reasoning tasks end-to-end include Goyal et al. (2021), Seraﬁni & d’Avila Garcez (2016), and Schlag & Schmidhuber (2018).”