CEPD Logic for Conflict and Error Detection in Constraint Networks
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Solution Overview
Problem
Current systems lack effective methods for predicting and detecting conflicts and errors in collaborative operational environments, as existing models and methodologies fail to differentiate between conflicts and errors, and do not adequately address the propagation of such issues, leading to undetected and unprevented errors that disrupt collaboration and productivity.
Innovation Solution
A method employing agent-based modeling to model systems with constraints that indicate potential conflicts and errors, using conflict and error prevention and detection (CEPD) logic to identify and prevent conflicts and errors before they occur, by analyzing the satisfaction of constraints and the propagation of errors between cooperative units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing error detection models and methodologies are used in collaborative systems, then basic error detection capability is provided, but conflicts and errors cannot be differentiated and their propagation cannot be adequately addressed
Solution Approach 1:
The patent segments error detection into two distinct methodologies: conflict detection (CD) for identifying inconsistencies in collaborative plans, and error detection (ED) for identifying execution deviations. This segmentation allows differentiated handling of conflicts and errors, improving both detection precision and collaboration reliability by addressing each type with specialized techniques.
Solution Approach 2:
The patent implements preliminary action through proactive conflict and error detection mechanisms that identify potential issues before they propagate through the collaborative system. By detecting conflicts in planning stages and errors in execution stages early, the system can prevent harmful propagation and maintain collaboration stability before disruptions occur.
2Measurement precision
If comprehensive constraint modeling is implemented to detect all potential conflicts and errors, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent divides the constraint modeling into separate conflict constraints and error constraints, each with their own detection methodologies. Conflict constraints model relationships between collaborative plans, while error constraints model deviations in plan execution. This segmentation reduces overall modeling complexity by allowing specialized, simpler models for each type rather than a single complex unified model.
Solution Approach 2:
The patent applies local quality by using different detection approaches tailored to specific constraint types: conflict detection uses plan relationship analysis for collaborative constraints, while error detection uses execution monitoring for operational constraints. Each local detection mechanism is optimized for its specific constraint type, improving accuracy without requiring a universally complex solution.
3Reliability
If real-time monitoring of all cooperative units is performed to prevent error propagation, then collaboration reliability improves, but computational resources and time consumption increase
Solution Approach 1:
The patent implements preliminary action by establishing constraint models and detection rules in advance for both conflicts and errors. During runtime, the system only needs to evaluate pre-defined constraints rather than performing comprehensive analysis, significantly reducing detection time while maintaining high reliability through continuous monitoring of critical constraint violations.
Solution Approach 2:
The patent extracts and monitors only the critical constraints that are most likely to cause conflicts or errors in collaborative systems. By focusing computational resources on evaluating specific high-risk constraints rather than all possible system states, the system achieves high detection reliability with reduced time and computational overhead.
Data Source
AI summary
Methods for interactively preventing and detecting conflicts and errors (CEs) through prognostics and diagnostics. Centralized and Decentralized Conflict and Error Prevention and Detection (CEPD) Logic is developed for prognostics and diagnostics over three types of real-world constraint networks: random networks (RN), scale-free networks (SFN), and Bose-Einstein condensation networks (BECN). A method is provided for selecting an appropriate CEPD algorithm from a plurality of algorithms having either centralized or decentralized CEPD logic, based on analysis of the characteristics of the CEPD algorithms and the characteristics of the constraint network.