Probabilistic Inference from Imprecise Knowledge via Logical Credal Networks
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Solution Overview
Problem
Current computational logic solutions assume simple, precise probabilities, failing to accurately reflect uncertainty or imprecision in data sets, limiting their effectiveness in making inferences from imprecise knowledge.
Innovation Solution
A method utilizing a logical credal network with a knowledge base of probabilistic statements and queries to make probabilistic inferences, allowing for the use of upper and lower bounds in probability distributions, and incorporating user input, natural language processing, and artificial intelligence to rate confidence levels and perform risk analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple, precise probabilities are used in computational logic, then the logic system is simple and easy to operate, but it fails to accurately reflect uncertainty or imprecision in data sets
Solution Approach 1:
The patent changes the parameter representation from simple precise probabilities to probability distributions with upper and lower bounds (imprecise probabilities). This allows the system to accurately reflect uncertainty in data sets while maintaining a relatively simple logical framework for reasoning about these imprecise probabilities.
Solution Approach 2:
The system dynamically handles probability representations by allowing probability distributions to be updated and refined as more information becomes available. The logical system adapts to work with both imprecise initial probabilities and more precise refined probabilities throughout the reasoning process.
2Reliability
If imprecise probability distributions with upper and lower bounds are used, then uncertainty in data sets is accurately reflected, but the complexity of the logical system increases
Solution Approach 1:
The patent introduces an intermediary layer between the imprecise probability distributions and the logical reasoning process. This intermediary framework provides structured rules and algorithms for manipulating probability bounds while maintaining logical consistency, thereby reducing the effective complexity despite working with imprecise probabilities.
Solution Approach 2:
The system segments the probability representation into distinct components (upper bound, lower bound, and implied mean or mode), allowing each component to be handled independently during logical reasoning. This segmentation simplifies the management of imprecise probabilities while maintaining overall reliability of inferences.
3Measurement precision
If probability distributions are used instead of simple probabilities, then imprecision and uncertainty are captured, but computational efficiency decreases
Solution Approach 1:
The system performs partial computations by focusing only on the essential aspects of probability distributions needed for specific logical inferences, rather than processing all possible probability details. This selective processing maintains sufficient precision for accurate inference while significantly improving computational efficiency.
Solution Approach 2:
The patent employs copying mechanisms where probability distributions are replicated and manipulated through standardized operations. By using copying instead of complex recalculations, the system maintains precision in probability representations while improving computational speed through efficient duplication and transformation of probability structures.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for probabilistic inference from imprecise knowledge is provided. The embodiment may include identifying a knowledge base of one or more statements and first probability distributions corresponding to each of the one or more statements. The embodiment may also include identifying one or more queries. The embodiment may further include determining logical inferences about and second probability distributions for queries from the one or more queries or statements from the one or more statements based on information in the knowledge base.