Natural Language Constraint Augmentation for Decision Policy Generation
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Solution Overview
Problem
Existing optimization models, such as machine learning (ML) models, face inefficiencies in design, generation, and augmentation, particularly when dealing with dynamic problems, as they require significant time and processing power, and changes in dynamic problems necessitate redesigning or retraining the models, leading to delays.
Innovation Solution
A system and method that utilizes natural language processing (NLP) to analyze constraints input in a natural language form, converting them into a mathematical form to generate an influence mapping, which is used to augment the optimization model and produce a decision policy, allowing for continuous improvement and efficient adaptation to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing optimization models are used for dynamic problems, then model accuracy can be maintained, but significant time and processing power are required for design, generation, and augmentation
Solution Approach 1:
The patent uses influence diagrams as simplified copies or representations of the complex optimization model structure. These influence diagrams capture the essential relationships and constraints without requiring full model retraining, enabling faster augmentation while preserving accuracy.
Solution Approach 2:
The patent segments the optimization model into distinct components: the base optimization model, the influence diagram representation, and the constraint integration layer. This segmentation allows independent processing and augmentation of specific model aspects without affecting the entire system, reducing overall processing time.
2Adaptability or versatility
If existing optimization models are retrained to adapt to changing dynamic problems, then model adaptability improves, but processing power and time requirements increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-processing constraints into influence diagram format before they are needed for model augmentation. This preparation work is done once and can be reused across multiple adaptation scenarios, avoiding repeated heavy processing when adapting to new conditions.
Solution Approach 2:
The influence diagram serves as an intermediary representation between the raw constraints and the optimization model. This intermediate format enables efficient constraint integration and model adaptation without requiring direct, computationally intensive retraining of the full optimization model.
3Ease of operation
If natural language constraints are converted to mathematical form and integrated into optimization models, then ease of operation improves, but device complexity increases
Solution Approach 1:
The influence diagram acts as an intermediary that bridges natural language constraints and mathematical optimization models. This intermediate representation layer handles the complexity of NLP processing and constraint mathematical formulation, shielding users from complexity while enabling natural language input.
Solution Approach 2:
The system performs self-service by automatically converting natural language constraints into mathematical form and integrating them into the optimization model without requiring manual mathematical formulation. The NLP processing and constraint transformation happen automatically, reducing user burden despite underlying system complexity.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to outputting an optimal decision policy base on informal knowledge input. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an analysis component that analyzes an input dataset comprising a constraint in a natural language form, and an augmentation component that generates an influence mapping comprising a constraint variable based on the constraint input. In an embodiment, an input dataset employed to support the influence mapping can comprise time-stamped tuple data comprising a state, an action and a reward. In an embodiment, an inference engine can generate an output policy in response to the constraint input and which output policy can be based on the constraint input and constraint variable.