Interaction Networks for Variable-Object State Prediction
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Solution Overview
Problem
Existing neural networks struggle to effectively analyze complex systems by decomposing scenarios into distinct objects and relations, leading to inefficiencies in processing and generalization across variable numbers of arbitrarily ordered objects and relations.
Innovation Solution
An interaction network that separates the analysis of relations between objects from the analysis of objects themselves, using multiple neural networks to predict future states by explicitly processing relationships as input, allowing for flexible and efficient processing of different interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing neural networks process complex systems as unified inputs, then they can maintain simple architecture, but they fail to effectively decompose scenarios into distinct objects and relations leading to poor generalization
Solution Approach 1:
The patent segments the complex system processing into distinct components: object analysis networks that process individual object properties and relation analysis networks that process interactions between objects. This segmentation allows the network to handle variable numbers of objects and relations by processing them as separate, modular units rather than requiring a complete reconfiguration for each different system configuration.
Solution Approach 2:
The patent creates universal object analysis and relation analysis networks that can process any object or relation type through standardized processing pathways. These networks are designed to handle arbitrary numbers of objects and relations by using the same core processing architecture, making the system multi-functional and adaptable to different system configurations without requiring architecture changes.
2Productivity
If neural networks process all objects and relations together, then processing is straightforward, but computational burden increases with complex systems
Solution Approach 1:
The patent divides the computational workload into separate object analysis and relation analysis components. Each object is processed by dedicated object analysis networks that extract properties independently, while relation analysis networks process interactions separately. This segmentation reduces the computational complexity from processing all objects and relations together to processing them in modular stages, improving efficiency for complex systems with many objects and relations.
3Adaptability or versatility
If the network processes variable numbers of arbitrarily ordered objects, then it can handle diverse scenarios, but standard neural network architectures struggle with this variability
Solution Approach 1:
The patent designs universal object analysis and relation analysis networks that process inputs through standardized pathways regardless of the number or ordering of objects. The framework uses consistent processing architecture that can accommodate variable inputs by processing each object and relation through the same modular networks, making the system universally applicable to diverse scenarios without increasing fundamental complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for predicting future states objects and relations in complex systems. One method includes receiving an input comprising states of multiple receiver entities and multiple sender entities, and attributes of multiple relationships between the multiple receiver entities and multiple sender entities; processing the received input using an interaction component to produce as output multiple effects of the relationships between the multiple receiver entities and multiple sender entities; and processing the states of the multiple receiver entities and multiple sender entities, and the multiple effects of the relationships between the multiple receiver entities and multiple sender entities using a dynamical component to produce as output a respective prediction of a subsequent state of each of the multiple receiver entities and multiple sender entities.