AI-Generated Rule Engine Modifications via Proxy Validation
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Solution Overview
Problem
Modifying rule engines is challenging due to complexity, compatibility issues with existing systems, and the need for explainable decision-making, particularly when using unsupervised AI models that lack transparency in their decision-making processes.
Innovation Solution
The use of generated proxy models that mimic the format and structure of existing rule engines, along with a model deployment criterion to validate and deploy modifications, ensuring explainability and effective integration with existing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If unsupervised AI models are used to modify rule engines, then automation and pattern recognition capabilities are improved, but explainability and transparency of decision-making deteriorate
Solution Approach 1:
The patent introduces an intermediary layer between the unsupervised AI model and the rule engine modification process. This intermediary includes components that translate the AI model's decisions into explainable rule modifications, maintaining automation while restoring transparency. The system generates multiple candidate modifications and selects those that can be explained in terms of understandable business rules.
2Adaptability or versatility
If rule engines are modified to improve functionality, then adaptability and decision-making capabilities are improved, but compatibility issues with existing systems arise
Solution Approach 1:
The patent implements preliminary validation and testing mechanisms before deploying modified rule engines to production. The system performs compatibility checks against existing systems and data structures, and maintains version control to allow rollback if compatibility issues arise. This enables functional improvements while preserving system reliability.
3Productivity
If complex rule sets are modified to enhance decision-making, then productivity and decision accuracy are improved, but system complexity and difficulty of maintenance increase
Solution Approach 1:
The patent segments complex rule sets into modular, independently manageable units. Each rule modification is treated as a discrete change that can be validated and deployed separately. This segmentation reduces overall system complexity while maintaining enhanced decision-making capabilities through structured rule organization.
Data Source
AI summary
Systems and methods provide a first deployment criterion for deploying modified decision engines. A first existing decision engine is accessed, as well as a first modified decision engine that includes rule data generated by an artificial intelligence model based on the first existing decision engine. A first difference between a first output and a first modified output is determined, where the first output is generated by the first existing decision engine and the first modified output is generated by the first modified decision engine. A first selected decision engine is deployed to process subsequent data items to produce subsequent outputs, based on whether the first difference satisfies first deployment criterion. When metric generated based on the subsequent outputs satisfies a criterion modification condition, the artificial intelligence model is used to generate a second deployment criterion, wherein a second selected rule-based decision engine is deployed based on the second deployment criterion.