Cognitive Pattern Knowledge Generation System
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Solution Overview
Problem
Existing knowledge-based systems struggle to adapt and act in novel situations without requiring extensive updates or external intervention, as they are limited by their initial knowledge base and lack the ability to generate situationally-relevant abstract patterns effectively.
Innovation Solution
The Cognitive Patterns Knowledge Generation (CPKG) system integrates theoretical concepts from Cognitive Science, such as Conceptual Blending, Dynamically Interpreted Perceptual Symbol Systems, and the human eye-brain visual system, to create a processor-based system that can generate and adapt cognitive patterns by vertically and horizontally blending abstract and concrete patterns, allowing it to understand and act in novel situations with minimal updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a knowledge-based system uses a fixed initial knowledge base, then the system structure is simple and easy to implement, but the system cannot adapt to novel situations without extensive external updates
Solution Approach 1:
The system implements dynamic knowledge base that can automatically generate new abstract patterns through vertical and horizontal blending operations. The knowledge base transitions from a static structure to a dynamic one that evolves autonomously by integrating concrete patterns from sensors with existing abstract patterns, eliminating the need for extensive external updates while maintaining adaptability to novel situations.
Solution Approach 2:
The system performs self-updating through automated pattern generation mechanisms. When new concrete patterns are received from sensors, the system automatically blends them with existing abstract patterns to create new abstract patterns, which are then stored back in the knowledge base. This self-service capability allows the system to adapt to novel situations without requiring external human intervention or extensive manual updates.
2Extent of automation
If the system requires extensive external updates to handle new situations, then the initial knowledge base can be simple, but the system requires significant human intervention and effort
Solution Approach 1:
The system automatically generates new abstract patterns by blending concrete sensor patterns with existing abstract patterns through vertical and horizontal operations. This self-updating mechanism eliminates the need for manual knowledge base updates, significantly reducing human intervention and the time required to adapt to new situations. The system continuously learns and evolves autonomously as it processes new sensory information.
Solution Approach 2:
The system implements a feedback loop where concrete patterns from sensors are continuously integrated with the knowledge base through pattern blending operations. The generated new abstract patterns are stored back in the knowledge base and immediately available for future pattern matching. This closed-loop feedback mechanism ensures the system automatically adapts to new situations in real-time without requiring external updates or human intervention.
3Adaptability or versatility
If the system uses detailed concrete patterns for all decisions, then the system can be simple to implement, but the system lacks flexibility in decision-making
Solution Approach 1:
The system introduces a vertical dimension to pattern processing by implementing vertical blending operations that integrate concrete patterns with abstract patterns at different levels of abstraction. This multi-layered approach allows the system to maintain both concrete details and abstract generalizations simultaneously, enabling flexible decision-making while managing complexity through hierarchical organization of patterns rather than processing all concrete details uniformly.
Solution Approach 2:
The system dynamically adjusts the level of abstraction in pattern processing based on the situation. Through vertical and horizontal blending operations, the system can generate abstract patterns that capture essential features while filtering out unnecessary details. This dynamic abstraction capability allows the system to make flexible decisions about novel situations without being overwhelmed by processing all concrete details, achieving adaptability while managing computational complexity.
Data Source
AI summary
A processor based system and method of generating cognitive pattern knowledge of a sensory input is disclosed. The method comprising the steps of receiving sensory input to create at least one concrete pattern, receiving at least one abstract pattern comprising abstract segments and vertically blending the concrete pattern with the abstract pattern by selectively projecting abstract segments to create a vertically blended pattern whereby the vertically blended pattern represents cognitive pattern knowledge of the sensory input. In some embodiments, the systems and methods further comprise creating a measure of a degree of vertical blending and when the measure of the degree of vertical blending exceeds a threshold, horizontally blending at least two abstract patterns to create a horizontally blended abstract pattern.