AI Rule Engine Regeneration Without Specific Training Data
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Solution Overview
Problem
Updating rule engines is challenging due to complexity, compatibility issues with existing systems, and the need for specific training data, which is time-consuming and resource-intensive.
Innovation Solution
A system that generates code for artificial intelligence models without requiring specific training data by using a regeneration model to mimic the format and structure of existing rule engines, applying consistency and functional checks to identify positive mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional training data approaches are used to update rule engines, then model accuracy can be improved, but the time and resources required for data collection and preparation increase significantly
Solution Approach 1:
The system performs preliminary actions by generating synthetic training data through simulations and domain knowledge models before actual model training is needed. This pre-generated data serves as a foundation that reduces the need for extensive real-world data collection later, thereby saving time while maintaining model accuracy.
Solution Approach 2:
The system introduces an intermediary layer consisting of domain knowledge models and simulation environments that mediate between the need for accurate training data and the time-consuming data collection process. These intermediaries generate realistic training scenarios without requiring actual field data collection, thus resolving the contradiction between accuracy and time investment.
2Reliability
If comprehensive training data is collected to cover all rule engine scenarios, then model reliability improves, but the complexity of data management and processing increases
Solution Approach 1:
The system segments the comprehensive training data requirement into manageable components by creating separate domain knowledge models for different rule engine scenarios. Each model handles specific aspects of the domain, generating targeted training data that collectively covers all scenarios without requiring a single massive, complex dataset that would be difficult to manage.
3Adaptability or versatility
If existing rule engines are updated with new functionality, then system adaptability improves, but compatibility issues with existing systems may arise
Solution Approach 1:
The system performs preliminary compatibility testing and validation through simulations before deploying updates to production rule engines. By pre-testing new functionality against existing system configurations and data formats, the system can identify and resolve compatibility issues before they affect actual operations, thus maintaining reliability while enabling adaptability.
Data Source
AI summary
Systems and methods for generating code for artificial intelligence models without requiring training data that is specific to model components and objectives. For example, the system may receive an original version of a rule engine. The system may input the original version, using a first input condition, into a regeneration model to generate a first regenerated version of the rule engine. The system may determine whether the first regenerated version includes a first hallucination based on comparing the first regenerated version to alternative versions of the rule engine, wherein each of the alternative versions were generated using a respective alternative input condition. The system may, in response to determining that the first regenerated version includes the first hallucination, determining whether the first hallucination comprises a positive mutation.