Decision Detection Constraint Graphs for Missing Rule Identification
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Solution Overview
Problem
Existing decision rule systems fail to effectively identify cases with missing decisions that violate decision requirements, leading to incomplete or incorrect application of decision policies in complex scenarios.
Innovation Solution
A method and system that build decision detection and requirement constraint graphs to identify missing decisions by analyzing the applicability of decision rules and requirements, using ghost rules to complete decision sets and ensure all required decisions are made.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If decision rule systems are used to determine decisions for cases, then decision automation is achieved, but the system fails to identify cases with missing decisions that violate decision requirements
Solution Approach 1:
The patent introduces constraint graphs as intermediary structures that mediate between decision rules and decision requirements. The decision detection constraint graph captures which decisions are made for each case, while the decision requirement constraint graph encodes which decisions are required. These graphical intermediaries enable systematic comparison to identify missing decisions, resolving the contradiction between automation and reliability.
Solution Approach 2:
The system implements feedback by using the constraint graphs to detect missing decisions and generate ghost rules that complete the decision sets. The feedback loop identifies violations of decision requirements and triggers rule generation to ensure all required decisions are made, thereby improving the reliability of the automated decision system.
2Measurement precision
If constraint graphs are built to identify missing decisions, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex problem of missing decision detection into two separate constraint graphs: one for decision detection and one for decision requirements. This segmentation allows each graph to focus on specific aspects, making the overall system more manageable and easier to implement while maintaining high detection accuracy.
3Reliability
If ghost rules are generated to complete decision sets, then decision requirement compliance is improved, but rule set complexity increases
Solution Approach 1:
The system performs preliminary action by generating ghost rules in advance to complete decision sets before actual decision execution. These ghost rules are created based on the analysis of constraint graphs and represent decisions that should be made but are missing from the original rule set, ensuring compliance with decision requirements.
Data Source
AI summary
Methods for identifying a case with a missing decision from a set of decision rules in violation of a decision requirement are provided. The set of decision rules and decision requirement are received, and a set of decisions made by the decision rules is obtained. A decision detection constraint graph is built, which represents, for each case used by the set of decision rules, whether each decision in the set of decisions is made or not by a decision rule in the set of decision rules. A decision requirement constraint graph is built from the decision requirement, which represents, for each case used by the set of decision rules, the decisions required. For each case used by the set of decision rules, the decision requirement constraint graph and the decision detection constraint graph for the case are used to identify if the case is a case with a missing decision.