# Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems

arXiv: https://arxiv.org/abs/2006.16225 · HTML: https://mlsanity.com/paper/2006.16225/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 30 of 48)

- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models](https://mlsanity.com/paper/2210.05861/) (2022)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Coordination Among Neural Modules Through a Shared Global Workspace](https://mlsanity.com/paper/2103.01197/) (2021)
- [Neural Production Systems: Learning Rule-Governed Visual Dynamics](https://mlsanity.com/paper/2103.01937/) (2021)
- [Causal Dynamics Learning for Task-Independent State Abstraction](https://mlsanity.com/paper/2206.13452/) (2022)
- [Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning](https://mlsanity.com/paper/2107.00848/) (2021)
- [Retrieval-Augmented Reinforcement Learning](https://mlsanity.com/paper/2202.08417/) (2022)
- [Discrete-Valued Neural Communication](https://mlsanity.com/paper/2107.02367/) (2021)
- [Neural Systematic Binder](https://mlsanity.com/paper/2211.01177/) (2022)
- [Fast and Slow Learning of Recurrent Independent Mechanisms](https://mlsanity.com/paper/2105.08710/) (2021)
- [Combining Modular Skills in Multitask Learning](https://mlsanity.com/paper/2202.13914/) (2022)
- [Dynamic Inference with Neural Interpreters](https://mlsanity.com/paper/2110.06399/) (2021)
- [Structured World Belief for Reinforcement Learning in POMDP](https://mlsanity.com/paper/2107.08577/) (2021)
- [Learning What and Where: Disentangling Location and Identity Tracking Without Supervision](https://mlsanity.com/paper/2205.13349/) (2022)
- [Object-centric architectures enable efficient causal representation learning](https://mlsanity.com/paper/2310.19054/) (2023)
- [Unsupervised Object-Based Transition Models for 3D Partially Observable Environments](https://mlsanity.com/paper/2103.04693/) (2021)
- [Transformers with Competitive Ensembles of Independent Mechanisms](https://mlsanity.com/paper/2103.00336/) (2021)
- [Unlocking Slot Attention by Changing Optimal Transport Costs](https://mlsanity.com/paper/2301.13197/) (2023)
- [A Complexity-Based Theory of Compositionality](https://mlsanity.com/paper/2410.14817/) (2024)
- [Compositional Attention: Disentangling Search and Retrieval](https://mlsanity.com/paper/2110.09419/) (2021)
- [Object-Centric Representation Learning with Generative Spatial-Temporal Factorization](https://mlsanity.com/paper/2111.05393/) (2021)
- [Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.03022/) (2022)
- [Test-time Adaptation with Slot-Centric Models](https://mlsanity.com/paper/2203.11194/) (2022)
- [Intrinsic Physical Concepts Discovery with Object-Centric Predictive Models](https://mlsanity.com/paper/2303.01869/) (2023)
- [Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior](https://mlsanity.com/paper/2305.19550/) (2023)
- [Cycle Consistency Driven Object Discovery](https://mlsanity.com/paper/2306.02204/) (2023)
- [Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement](https://mlsanity.com/paper/2303.11373/) (2023)
- [Spatial Entropy as an Inductive Bias for Vision Transformers](https://mlsanity.com/paper/2206.04636/) (2022)
- [Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases](https://mlsanity.com/paper/2410.15728/) (2024)

## Abstract (arXiv)

Modeling a structured, dynamic environment like a video game requires keeping track of the objects and their states declarative knowledge) as well as predicting how objects behave (procedural knowledge). Black-box models with a monolithic hidden state often fail to apply procedural knowledge consistently and uniformly, i.e., they lack systematicity. For example, in a video game, correct prediction of one enemy's trajectory does not ensure correct prediction of another's. We address this issue via an architecture that factorizes declarative and procedural knowledge and that imposes modularity within each form of knowledge. The architecture consists of active modules called object files that maintain the state of a single object and invoke passive external knowledge sources called schemata that prescribe state updates. To use a video game as an illustration, two enemies of the same type will share schemata but will have separate object files to encode their distinct state (e.g., health, position). We propose to use attention to determine which object files to update, the selection of schemata, and the propagation of information between object files. The resulting architecture is a drop-in replacement conforming to the same input-output interface as normal recurrent networks (e.g., LSTM, GRU) yet achieves substantially better generalization on environments that have multiple object tokens of the same type, including a challenging intuitive physics benchmark.