Universal Self-Learning System with Inference Loop and Contradiction Checking
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Solution Overview
Problem
Current artificial intelligence systems are limited to specific technical fields and cannot independently learn or solve problems across multiple fields, making it challenging to create a universal AI system capable of self-learning in various domains.
Innovation Solution
A universal self-learning system is developed, comprising a knowledge base and an inference engine, which performs continuous inference to form chains and loops, checks for contradictions, and integrates new knowledge, maintaining knowledge consistency through a dynamic inference cycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a universal self-learning system performs continuous multi-round inference to expand knowledge, then the system's learning capability and knowledge coverage improve, but the risk of generating wrong knowledge or knowledge with lower accuracy increases
Solution Approach 1:
The patent implements a feedback mechanism where the inference engine continuously checks generated knowledge against the knowledge base. The judging machine provides feedback by identifying contradictions between new inference knowledge and existing knowledge, allowing the system to correct errors and maintain knowledge accuracy while expanding learning capabilities through multi-round inference.
Solution Approach 2:
The patent introduces a judging machine as an intermediary component between the inference engine and the knowledge base. This intermediary checks for contradictions and filters wrong knowledge before it is added to the knowledge base, enabling the system to expand knowledge reliably without compromising accuracy.
2Adaptability or versatility
If the system integrates new knowledge into the knowledge base to expand knowledge coverage, then the system's versatility improves, but the complexity of maintaining knowledge consistency and detecting contradictions increases
Solution Approach 1:
The patent segments the knowledge management function into distinct components: an inference engine for generating new knowledge, a judging machine for checking contradictions, and a knowledge base for storage. This segmentation allows the system to handle knowledge integration systematically, reducing the complexity of maintaining consistency while expanding knowledge coverage.
Solution Approach 2:
The judging machine serves as an intermediary that automates the complex task of contradiction detection and knowledge consistency maintenance. By delegating this function to a specialized component, the system can integrate new knowledge efficiently without manually managing the complexity of consistency checks.
3Productivity
If the inference engine performs multiple rounds of continuous inference, then the system's ability to discover new knowledge improves, but the time required for the inference process increases
Solution Approach 1:
The patent implements preliminary action by having the judging machine check for contradictions at each inference round before adding new knowledge to the base. This preliminary checking prevents error propagation and reduces the need for extensive re-inference later, thereby reducing total inference time while maintaining productive knowledge discovery.
Solution Approach 2:
The continuous feedback mechanism from the judging machine allows the system to identify and correct knowledge errors early in the inference process. This feedback prevents wasteful multi-round inference on incorrect knowledge paths, improving overall knowledge discovery efficiency while reducing unnecessary inference time.
Data Source
AI summary
The present disclosure discloses a self-learning method based on a universal self-learning system. The self-learning method includes: performing inference based on a knowledge in a knowledge base to generate an inference knowledge; performing inference on the basis of acquired nth inference knowledge to obtain (n+1)th inference knowledge, and performing several rounds of inference continuously to form an inference chain; and forming an inference loop if the knowledge inferred according to the inference chain already exists in the inference chain. Through the self-learning method based on the universal self-learning system, the inference loop and an inference cycle about knowledge can be constructed in the knowledge base, and the self-consistency and activity of knowledge in the knowledge base can be maintained through the dynamic and sustainable inference cycle with high activity.