Adaptive Cognitive System for Dynamic Rule Generation
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Solution Overview
Problem
Current artificial intellect systems rely on pre-defined knowledge bases and rules, limiting their adaptability and predictability, and self-education methods only fill statistical data without altering existing rules, making them inefficient in dynamic environments.
Innovation Solution
A system that processes sensor information from various streams, including audio, video, and temperature data, to generate commands for executive organs, adapting to the environment by correlating information units and optimizing command sequences through a trial-and-error method, using meta-control channels and data compression to manage large volumes of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-defined knowledge bases and rules are used, then system predictability is improved, but system adaptability deteriorates
Solution Approach 1:
The patent implements dynamic rule generation where the system transitions from static pre-defined rules to dynamically generated rules based on environmental feedback. The cognitive system continuously adapts its rule set through trial-and-error learning, allowing rules to be created, modified, or deleted based on their effectiveness in achieving target states, thus resolving the contradiction between predictability and adaptability.
Solution Approach 2:
The system performs self-education by automatically generating and refining its own knowledge base without external intervention. Through the trial-and-error method, the system evaluates its own performance and autonomously adjusts its rule set, enabling it to maintain predictability through structured learning while simultaneously improving adaptability to new environments.
2Quantity of substance
If self-education methods fill statistical data, then system knowledge is improved, but rule alteration capability deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system evaluates the results of executing commands against target states and uses this feedback to generate new rules or modify existing ones. The trial-and-error process continuously refines the rule set based on performance feedback, allowing the system to expand its knowledge base while simultaneously developing rule alteration capabilities.
Solution Approach 2:
The patent introduces an intermediary cognitive layer that processes statistical data and translates it into actionable rules. This intermediary system analyzes patterns in the data and generates rules that bridge the gap between raw statistical knowledge and executable commands, enabling both knowledge accumulation and rule creation.
3Adaptability or versatility
If trial-and-error method is used for adaptation, then system adaptability is improved, but system complexity deteriorates
Solution Approach 1:
The patent segments the complex adaptation process into distinct modular components: perception modules for input processing, cognitive modules for rule generation and evaluation, and execution modules for command implementation. This segmentation allows the trial-and-error method to be applied in a structured manner, improving adaptability while managing complexity through modular architecture.
Solution Approach 2:
The system manages complexity by adjusting parameters such as the number of trial iterations, confidence thresholds for rule adoption, and priority levels for different rules. By controlling these parameters, the system can tune the balance between adaptability and complexity, preventing runaway complexity while maintaining effective learning.
4Loss of information
If large volumes of sensor data are processed, then system perception capability is improved, but data management efficiency deteriorates
Solution Approach 1:
The patent extracts only the essential features and relevant information from large volumes of sensor data rather than processing all raw data. The cognitive system identifies and extracts key characteristics needed for rule generation and decision-making, discarding redundant information, thus improving perception capability while maintaining data management efficiency.
Solution Approach 2:
The system performs preliminary processing and filtering of sensor data before main analysis, pre-organizing information into structured formats that facilitate efficient rule generation. By preparing data in advance through feature extraction and relevance filtering, the system reduces the computational burden of subsequent processing while maintaining comprehensive perception.
Data Source
AI summary
An adaptive cognitive method has been revealed where each distinguishable information unit, entering a given input channel, receives a unique label /identifier/ which serves as a center for the dynamic building of a structure for the presentation of knowledge on the respective information unit. A basic marker for the analysis of the correlation between the separate information units is the time quantum which is recorded—for each change—in the data base. The time quantum is the number of the shortest periods of time for the system, which have so far passed.