Reasoning Engine Hypothesis Validation Through Active Data Acquisition
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Solution Overview
Problem
Existing AI techniques fail to effectively utilize vast amounts of digital data for automated reasoning and validation of inferences, requiring human intervention to interpret patterns and relationships.
Innovation Solution
A reasoning engine that collects environment data through various modalities, applies multiple reasoning techniques to generate hypotheses, and validates these hypotheses by influencing data acquisition to establish their validity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional AI techniques are used to analyze data, then correlations among existing data sets can be found, but human intervention is still required to reason or infer the meaning behind the patterns
Solution Approach 1:
The system enables self-service by allowing the computing infrastructure to automatically reason about and validate its own data without requiring human intervention. The reasoning engine processes digital data, generates hypotheses about relationships and patterns, and validates these hypotheses through automated experimentation, making the system self-sufficient in extracting meaningful insights from vast datasets.
Solution Approach 2:
The validation module provides feedback by testing generated hypotheses against the digital data and adjusting reasoning processes accordingly. This feedback loop allows the system to learn from its own operations, refine its reasoning capabilities, and progressively improve its ability to accurately interpret complex data relationships autonomously.
2Productivity
If reasoning techniques are applied to generate hypotheses, then automated validation can be performed, but the complexity of the reasoning and validation process increases
Solution Approach 1:
The reasoning engine is segmented into distinct functional modules: a reasoning module that generates hypotheses about relationships and patterns, and a validation module that tests these hypotheses through controlled experimentation. This segmentation allows each module to specialize in specific tasks, making the overall complex process more manageable and maintainable while enabling automated correlation discovery.
Solution Approach 2:
The reasoning engine is designed as a universal system that can process multiple types of digital data across diverse environments and domains. The same core reasoning and validation mechanisms can be applied to various data modalities and contexts, reducing the need for separate specialized systems and simplifying the overall architecture while maintaining high productivity in correlation discovery.
3Reliability
If multiple reasoning approaches are used to explore hypotheses, then validation accuracy improves, but the time required for analysis increases
Solution Approach 1:
The validation module employs partial action by selectively testing only the most promising hypotheses and using a subset of available data for validation when sufficient evidence is found. This approach maintains high reliability by thorough validation of critical hypotheses while reducing overall time consumption by avoiding exhaustive testing of all possible hypotheses, thus balancing accuracy and efficiency.
Data Source
AI summary
A reasoning engine is disclosed. Contemplated reasoning engines acquire data relating to one or more aspects of various environments. Inference engines within the reasoning engines review the acquire data, historical or current, to generate one or more hypotheses about how the aspects of the environments might be correlated, if at all. The reasoning engine can attempt to validate the hypotheses through controlling acquisition of the environment data.