Noisy-OR Inference with Outcome Ordering for Probable Cause Detection
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Solution Overview
Problem
Existing methods for determining the most probable cause from multiple outcomes, such as Noisy-OR and Noisy-AND models, face challenges in handling overlapping causes and efficiently inferring causes from observable outcomes, particularly in complex scenarios like medical diagnosis and alarm systems, where computational expense and accuracy are concerns.
Innovation Solution
The proposed solution involves a Noisy-OR model with outcome ordering indices, which categorizes observed outcomes into sets for exact and variational inference, using a knowledge graph to establish causal relationships and employing a combination of exact and variational inference methods to quickly and accurately determine the most probable cause.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If strictly exact inference methods are used to determine the most probable cause, then measurement precision is improved, but computing time increases exponentially
Solution Approach 1:
The patent segments the set of observed outcomes into two subsets: a first subset processed using exact inference methods and a second subset processed using variational inference methods. This segmentation allows the system to apply computationally intensive exact inference only where necessary while using faster variational inference for other outcomes, thereby reducing overall computing time while maintaining inference accuracy for critical cases.
2Productivity
If variational inference methods are used to reduce computational complexity, then computing time is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent applies different inference methods to different subsets of outcomes based on their specific characteristics. The first subset of outcomes is processed using exact inference methods to ensure high precision, while the second subset is processed using variational inference methods to achieve faster computation. This local differentiation of inference quality matches the computational needs of each outcome subset.
3Ease of operation
If traditional Noisy-OR models are used without outcome ordering, then ease of operation is maintained, but device complexity increases due to handling overlapping causes
Solution Approach 1:
The patent performs preliminary ordering of observed outcomes before applying inference methods. By establishing an ordered sequence of outcomes in advance, the system prepares the data structure needed for efficient processing, allowing exact and variational inference methods to be applied more effectively to subsets of the ordered outcomes, thereby reducing computational complexity.
Data Source
AI summary
Systems and methods are disclosed for question generation to infer the most probable cause from the observable outcome and known Noisy-OR causal relations. In embodiments, the outcomes are sorted by indices according to an order including but not limited to the outcomes' natural frequency order, expert-labeled order, machine-learning derived order, etc. According to their assigned indices, in embodiments, observed outcomes with lower indices are assigned for exact inference while observed outcomes with higher indices are assigned for variational inference. In embodiments, results of exact inference and variational inference are combined to predict the most probable cause. The unique combination of exact inference and variational inference according to outcome indices makes the probable cause inferring process faster.