Dynamic Constraint Solver Hierarchical Union Operator
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Solution Overview
Problem
Existing constraint satisfaction problem (CSP) solvers are inadequate for large-scale problems with conditional structures, as they fail to provide significant space savings and do not allow segmentation of related variables into sub-problems, limiting their effectiveness in representing and solving dynamic constraint satisfaction problems.
Innovation Solution
A dynamic constraint solver system that employs a hierarchical union operator and corresponding constraint to compute the cardinality of a hierarchical union, determining an included set and an excluded set, and propagating these sets to participating ports, allowing for efficient aggregation and reduction of variables in a nested hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing CSP solvers are used for large-scale problems with conditional structures, then the problems can be solved, but significant space savings are not achieved and segmentation of related variables into sub-problems is not allowed
Solution Approach 1:
The patent segments large CSP problems into smaller sub-problems organized in a hierarchical structure with multiple levels. Each level contains sub-problems that can be independently managed and solved. This segmentation allows the system to handle large-scale problems by breaking them down into manageable units while maintaining the ability to represent conditional structures and achieve space savings through selective instantiation of sub-problems based on active variables.
2Adaptability or versatility
If Conditional CSP approach is used to minimize large CSP problems, then variable activity states can be managed, but no significant space savings are provided and segmentation of related variables into sub-problems is not achieved
Solution Approach 1:
The patent implements a nested hierarchical structure where sub-problems are organized in multiple levels, with each level containing sub-problems that can be selectively instantiated. This nesting allows the system to maintain adaptability through variable activity states while achieving space savings by only instantiating and storing sub-problems that are currently active or relevant to the solution, rather than maintaining all possible sub-problems in memory simultaneously.
3Adaptability or versatility
If Generative CSP approach is used to extend Conditional CSP, then component concepts are introduced, but real space savings are still not provided
Solution Approach 1:
The patent introduces dynamic instantiation and de-instantiation of sub-problems based on the activity state of variables. Sub-problems are dynamically created and removed from memory as needed during the solving process, rather than pre-defining all components. This dynamic approach allows the system to maintain versatile component structure representation while achieving real space savings by ensuring that only currently relevant sub-problems occupy memory space.
Data Source
AI summary
A dynamic constraint solver system for solving a constraint satisfaction problem model includes a plurality of ports. The system defines a hierarchical union that includes all problems in a lower port that is in a problem under another port in the model. The system generates a constraint that computes a cardinality of the hierarchical union and determines an included set and an excluded set for the hierarchical union. The system then propagates the included set and excluded set to participating ports of the hierarchical union.