Programmatic Application Rule Refinement Using Feedback Trees
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Solution Overview
Problem
Programmatic algorithms often operate using incomplete or missing constraints and rules, leading to inefficient and resource-wasting generation of incorrect outputs.
Innovation Solution
A method and system that builds an ensemble of decision trees using feedback data to extract new rules from a programmatic application, refining output by identifying and removing instances of poor output, thereby adjusting the application's operation to minimize negative feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If programmatic algorithms use predetermined constraints and rules, then the system operation is straightforward, but the constraints and rules are often incomplete and missing implicit constraints
Solution Approach 1:
The system implements feedback loops where outputs are evaluated against feedback data (negative feedback indicating poor output). This feedback is used to iteratively refine and update the constraints and rules, allowing the system to learn from its mistakes and improve completeness without requiring manual specification of all constraints upfront.
Solution Approach 2:
The system performs self-learning by automatically generating updated constraints and rules from feedback data without external intervention. The algorithm autonomously identifies patterns in negative feedback and translates them into refined constraints, enabling the system to self-improve its rule completeness over time.
2Productivity
If the programmatic application generates outputs using existing rules, then the process is efficient, but incorrect outputs are generated due to incomplete constraints
Solution Approach 1:
Feedback data indicating poor output is collected and used to identify patterns of incorrect outputs. The system analyzes this feedback to generate refined constraints that specifically address the identified errors, thereby improving output accuracy while maintaining efficient generation processes through automated learning rather than manual rule adjustment.
Solution Approach 2:
The system performs preliminary analysis of feedback data to identify patterns of incorrect outputs before generating updated constraints. This preliminary action allows the system to proactively correct potential errors in future output generation, improving reliability by addressing issues before they recur.
3Measurement precision
If feedback data is collected and analyzed to improve rules, then the accuracy of output prediction increases, but the complexity of the system increases
Solution Approach 1:
The system autonomously processes feedback data and generates updated constraints without requiring complex external analysis tools or manual intervention. The algorithm self-manages the entire feedback processing pipeline, from collecting negative feedback to analyzing patterns and generating refined constraints, thereby improving prediction accuracy while minimizing the addition of external system complexity.
Solution Approach 2:
The system introduces an intermediary learning layer that translates feedback data into refined constraints. This intermediary component acts as a mediator between the raw feedback data and the rule system, automatically processing and transforming feedback into actionable constraint updates, thereby improving accuracy without directly increasing the complexity of the core rule engine.
4Reliability
If the system updates constraints based on feedback, then the quality of output improves, but more computational resources are consumed
Solution Approach 1:
The system applies partial updates to constraints based on feedback, focusing computational resources on refining only the specific constraints that are identified as needing improvement. Rather than completely reprocessing all constraints with each feedback cycle, the system selectively updates only the relevant portions, thereby improving output quality while consuming fewer computational resources than a full reprocessing approach would require.
Data Source
AI summary
A computing device receives: input and output from a programmatic application that refines given resource usage; and feedback data indicating feedback regarding the output from the programmatic application, the feedback data including negative feedback indicating a subset of the output that was changed or indicated as poor output. The computing device builds a prediction model comprising an ensemble of decision trees using the input and the output. Based on the feedback data, the computing device extracts, from the prediction model, new rules, which indicate instances of when the output is further refinable, the new rules covering at least a given portion of the output associated with negative feedback. The computing device adjusts the programmatic application, based on the new rules.