Reasoning Engine Automating Ambient Data Inference and Validation
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Solution Overview
Problem
Existing artificial reasoning technologies are limited in their ability to automatically infer relationships within data sets and validate these inferences without human intervention, failing to fully leverage the potential of ambient data for reasoning.
Innovation Solution
A reasoning engine that utilizes a data interface to acquire environment data, aggregates it through inference engines, and validates hypotheses using a validation module, allowing the infrastructure to function as a reasoning engine based on collected ambient data.
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 automated reasoning and validation of inferences cannot be achieved
Solution Approach 1:
The system segments the reasoning process into distinct modules: data acquisition, hypothesis generation through multiple reasoning techniques (abductive, deductive, inductive, probabilistic), and validation. This segmentation allows automated reasoning while preserving meaning through structured processing at each stage.
Solution Approach 2:
The system implements feedback loops where validation results feed back into the hypothesis generation process. The validation module tests hypotheses against additional data and refines reasoning techniques based on validation outcomes, enabling automated reasoning that preserves and refines meaning iteratively.
2Ease of operation
If reasoning techniques are applied to derive facts, then useful patterns can be established, but human intervention is still required to infer meaning
Solution Approach 1:
The system employs multiple reasoning techniques (abductive, deductive, inductive, probabilistic) within a single unified platform. This multi-functionality enables automated validation across different types of inferences without requiring separate systems, reducing operational complexity while maintaining comprehensive reasoning capabilities.
Solution Approach 2:
The validation module acts as an intermediary between hypothesis generation and final conclusions. It mediates the complex reasoning processes by systematically testing hypotheses against validation criteria, automating the validation task while managing the complexity of the underlying reasoning infrastructure.
3Adaptability or versatility
If multiple reasoning approaches are provided in response to an inquiry, then more comprehensive analysis can be achieved, but system complexity increases
Solution Approach 1:
The system dynamically selects and applies appropriate reasoning techniques based on the specific inquiry and data characteristics. Rather than rigidly applying all reasoning approaches, the system adapts its reasoning methodology to match the problem context, achieving versatility while managing complexity through dynamic adaptation.
Solution Approach 2:
The system changes parameters of the reasoning process (such as the type of reasoning technique applied, the data sources consulted, and the validation criteria used) based on the specific inquiry. This parameter-based adaptation allows multiple reasoning approaches to be available when needed while keeping the system manageable through systematic parameter control.
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.