Vector-Symbolic Representations for Continuous Space Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods and systems for encoding and processing structured representations struggle to effectively represent continuous structures and blend continuous and discrete structures, limiting their ability to model spatial, temporal relationships, and simulate dynamical systems.
Innovation Solution
The introduction of spatial semantic pointers (SSPs) and associated binding, unbinding, and transformation subsystems that perform fractional binding and unbinding operations, allowing for the representation and manipulation of continuous structures using vector-symbolic representations, enabling the creation of high-dimensional vector representations that include continuous relationships between elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional computer algorithms operate on symbols to represent discrete structures, then efficiency in representing graphs, lists, and trees is improved, but ability to represent continuous structures such as two-dimensional planes deteriorates
Solution Approach 1:
The patent combines discrete symbolic representation with continuous vector representation by mapping symbols to vectors in a high-dimensional space. This merging allows the system to simultaneously represent discrete structures (through symbolic operations) and continuous structures (through vector operations in continuous space), resolving the contradiction between efficiency for discrete structures and adaptability for continuous structures
Solution Approach 2:
The patent changes the parameter space by introducing continuous vector representations with varying dimensions and magnitudes. By allowing vectors to exist in continuous space rather than discrete symbol space, the system gains the ability to represent continuous structures while maintaining symbolic operations through vector algebra
2Adaptability or versatility
If artificial neural network algorithms process continuous structures such as planes and images, then ability to handle continuous data is improved, but natural representation of discrete symbols deteriorates
Solution Approach 1:
The patent introduces vectors in high-dimensional space as an intermediary between discrete symbols and continuous neural network processing. Symbols are mapped to vectors, allowing neural networks to process continuous structures while the vector representations maintain discrete symbolic properties through operations like binding and unbinding, thus serving as a mediator between the two paradigms
3Device complexity
If existing vector symbolic architectures use circular convolution to compress symbolic items into fixed width vector representations, then ability to represent compositional structures is improved, but ability to represent continuous relationships between entities deteriorates
Solution Approach 1:
The patent introduces dynamic vector representations that can vary in dimension and magnitude, replacing the static fixed-width vectors of traditional VSAs. This dynamic approach allows vectors to adapt their representation to capture continuous relationships while maintaining the compositional structure benefits of fixed-width representations when needed, enabling both compression and continuous representation
Data Source
AI summary
The present invention relates to methods and systems for encoding and processing representations that include continuous structures using vector-symbolic representations. The system is comprised of a plurality of binding subsystems that implement a fractional binding operation, a plurality of unbinding subsystems that implement a fractional unbinding operation, and at least one input symbol representation that propagates activity through a binding subsystem and an unbinding subsystem to produce a high-dimensional vector representation of a continuous space.