Language-Independent Reasoning Engine with Tree Decomposition
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Solution Overview
Problem
Current artificial agent systems are specialized and tailored to specific application domains, requiring different computational methodologies for natural languages, images, and formal logic, limiting their ability to perform language-independent reasoning and problem solving.
Innovation Solution
A problem solving and reasoning engine that decomposes user queries into sub-queries arranged in a tree structure, allowing for language-independent execution and assembly of results, using forward and backward reasoning, and learning rules from the tree structure to determine answers within a knowledge base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized computational methodologies are used for each application domain (natural language, images, formal logic), then the system can achieve high accuracy in specific domains, but the system complexity increases and language-independent reasoning capability is lost
Solution Approach 1:
The patent applies universality by creating a single problem-solving architecture that can handle multiple application domains (natural language, images, formal logic) through a common computational framework. The system uses unified components including a problem representation module, decomposition module, sub-problem solver, and answer composition module that work across different input types without requiring domain-specific specialized methodologies, thereby reducing system complexity while maintaining reasoning accuracy
Solution Approach 2:
The patent applies segmentation by breaking down complex problems into smaller sub-problems through a decomposition module. This allows the system to handle diverse input types by recursively decomposing them into standardized sub-problems that can be solved using the same computational framework, enabling language-independent reasoning while maintaining high accuracy through systematic problem breakdown
2Reliability
If multiple specialized systems are used for different application domains, then each domain can be optimized independently, but the overall system adaptability decreases
Solution Approach 1:
The patent creates a universal problem-solving system that maintains reliability across different domains by using a consistent computational framework. The system achieves domain optimization not through specialized methodologies but through a unified architecture that reliably handles natural language, images, and formal logic using the same problem decomposition and solving mechanisms, thereby improving adaptability while maintaining domain-specific performance
3Measurement precision
If completely different computational methodologies are used for natural language, images, and formal logic, then each methodology can be optimized for its specific input type, but the system cannot perform language-independent reasoning
Solution Approach 1:
The patent enables language-independent reasoning by segmenting the problem-solving process into language-neutral stages. The decomposition module breaks down input problems into standardized sub-problems that are independent of the input language or format. This segmentation allows the same computational framework to process natural language, images, and formal logic uniformly, achieving both input processing accuracy and language independence
Solution Approach 2:
The patent introduces an intermediary problem representation layer that mediates between diverse input types and the core solving mechanism. This intermediate representation allows different input formats (natural language, images, formal logic) to be translated into a unified problem structure that can be processed by the same decomposition and solving algorithms, thereby enabling language-independent reasoning while maintaining input-specific accuracy
Data Source
AI summary
A method of improving computing efficiency of a computing device for language-independent problem solving and reasoning includes receiving a query from a user, which is decomposed into one or more sub-queries arranged according to a tree structure. The one or more sub-queries are executed in a knowledge base. The results of the executed one or more sub-queries are received and composed into a query response. The query response is transmitted to the user.