Constraint Solver Identifies Top Constraint Expressions
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Solution Overview
Problem
Existing constraint satisfaction problem (CSP) solvers fail to effectively represent and reason about conditional structures and provide significant space savings in large problems, limiting their ability to handle large component-based systems efficiently.
Innovation Solution
A constraint solver that identifies and manages top constraint expressions within a network, utilizing internal constraints and dynamic substructure to enhance inference power and reduce network complexity, allowing for better conflict resolution and understanding of CSP networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If known CSP solvers are used to represent large component-based systems, then the system can handle basic constraint satisfaction problems, but the system cannot effectively represent conditional structures or provide space savings in large problems
Solution Approach 1:
The patent segments the CSP representation by introducing sub-problems that can be conditionally included or excluded from the constraint network. Each sub-problem represents a modular component that can be activated based on conditional logic, allowing the system to represent conditional structures while reducing the overall space requirements by only including relevant sub-problems in the active constraint network.
Solution Approach 2:
The patent implements dynamic inclusion/exclusion of sub-problems in the constraint network based on conditional satisfaction. The system dynamically adjusts the constraint network composition by including sub-problems when their conditions are met and excluding them when not, providing both adaptability for conditional representation and space savings by maintaining a compact active constraint network.
2Reliability
If all variables and constraints are included in the CSP network, then complete problem representation is achieved, but network complexity and processing difficulty increase
Solution Approach 1:
The patent divides the complete problem into multiple sub-problems that can be independently managed. Each sub-problem represents a manageable portion of the overall constraint network, reducing individual network complexity while maintaining complete problem representation through the collective set of sub-problems that can be conditionally activated.
Solution Approach 2:
The patent applies local quality by allowing different regions of the constraint network (sub-problems) to have different levels of activation and complexity. Rather than uniformly including all constraints, the system selectively activates sub-problems based on local conditions, reducing overall network complexity while preserving necessary problem representation in active regions.
3Quantity of substance
If conditional CSP approaches are used to minimize large problems, then some space reduction is achieved, but no significant space savings are provided and segmentation into sub-problems is not enabled
Solution Approach 1:
The patent explicitly enables segmentation into sub-problems, where each sub-problem is a distinct, conditionally activatable unit. This segmentation provides both space savings by allowing selective inclusion of sub-problems and adaptability by enabling conditional logic to determine which sub-problems are active, overcoming the limitations of traditional conditional CSP approaches.
Solution Approach 2:
The patent implements dynamic sub-problem inclusion/exclusion mechanisms that go beyond static conditional CSP. The system can dynamically adjust which sub-problems are included in the active constraint network based on runtime conditions, providing significant space savings by excluding irrelevant sub-problems while maintaining the ability to represent complex conditional structures.
Data Source
AI summary
A constraint solver solves a constraint satisfaction problem (“CSP”) that includes a network of variables and constraints. The solver receives a sub-expression and determines if the sub-expression is an internal constraint of the CSP. The internal constraint has a supporting operator expression. If the sub-expression is an internal constraint, the solver determines any or all top constraint expressions associated with the supporting operator expression.