Real-Valued Logic Reasoning for Uncertainty Handling
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Solution Overview
Problem
Current computing systems face challenges in performing accurate automated reasoning due to the brittleness of classical logic and the limitations of small, noisy datasets in machine learning, which can lead to errors in reasoning with knowledge extracted from real-world data.
Innovation Solution
The method involves converting first-order logic formulas into real-valued logic formulas and using probability intervals to provide interval conditional probabilities, allowing for probabilistic inference through logical neural networks and credal networks, thereby accounting for uncertainty and improving reasoning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical logic is used for automated reasoning, then reasoning speed is fast, but reasoning accuracy deteriorates due to brittleness
Solution Approach 1:
The patent transforms classical binary logic (0 or 1) into real-valued logic where truth values are continuous real numbers between 0 and 1. This parameter change allows for gradual truth values and probabilistic reasoning, resolving the contradiction by improving reasoning accuracy through uncertainty representation while maintaining computational tractability through standardized mathematical operations.
Solution Approach 2:
The patent combines multiple logical systems (classical logic, fuzzy logic, and probabilistic reasoning) into a unified real-valued logic framework. This composite approach integrates the strengths of different logical systems to achieve both accuracy through uncertainty handling and efficiency through standardized mathematical operations.
2Reliability
If machine learning is used with small noisy datasets, then adaptability is improved, but reasoning reliability deteriorates
Solution Approach 1:
The patent introduces probability intervals as an intermediary layer between raw data and logical reasoning. This intermediary representation filters and regularizes noisy data by expressing uncertainty in a structured mathematical form, thereby improving reasoning reliability while preserving the ability to handle diverse and adaptive data scenarios.
Solution Approach 2:
The patent changes the parameter representation from point estimates to probability intervals, transforming how data is handled. This parameter transformation allows the system to accommodate noisy and small datasets by expressing uncertainty explicitly, improving reliability without sacrificing adaptability to different data conditions.
3Reliability
If probability intervals are used instead of single values, then uncertainty handling is improved, but computational complexity increases
Solution Approach 1:
The patent changes the parameter representation from single point values to probability intervals, enabling explicit uncertainty handling. Despite this increase in representational complexity, the use of standardized mathematical operations and the structured nature of interval arithmetic keeps computational complexity manageable, resolving the contradiction by improving reliability through uncertainty representation.
Data Source
AI summary
Embodiments are provided for providing enhanced routing in a computing system by a processor. All first-order logic formulas may be converted into real-valued logic formulas. A probabilistic inference is executed using the real-valued logic formulas and one or more probability intervals associated with an atomic formulae in a knowledge base to provide an interval conditional probability indicating that a first predicate condition is true based one or more alternative predicates being true.