Augmented Knowledge Base Reasoning with Evidence Sets
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Solution Overview
Problem
Existing knowledge-based systems face challenges in effectively reasoning with uncertainties and incompleteness, as they often rely on inadequate methods for handling uncertain, vague, or incomplete information, leading to inconsistencies and anomalies.
Innovation Solution
The development of an augmented knowledge base (AKB) system that represents knowledge as objects in the form E→A, where A is a rule and E is a set of evidences supporting the rule, allowing for deductive and inductive reasoning, and providing methods for constructing consistent higher-order knowledge bases to ensure accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional knowledge-based systems use simple belief or certainty factor mappings for reasoning, then the system is easier to operate, but the reasoning accuracy and reliability deteriorate due to inconsistencies and anomalies
Solution Approach 1:
The patent segments the knowledge base into distinct components: rules (A), evidences (E), and their mappings (E→A). This segmentation allows for more precise control and analysis of each component's contribution to reasoning, thereby improving reliability without overwhelming complexity.
Solution Approach 2:
The patent introduces a new dimension by mapping sets of evidences to rules rather than simple belief values. This dimensional shift from scalar belief factors to set-based evidence mappings enables more nuanced reasoning while maintaining systematic structure.
2Adaptability or versatility
If traditional knowledge bases associate each knowledge piece with a single belief value, then the system structure is simpler, but the ability to handle uncertain and incomplete information deteriorates
Solution Approach 1:
The patent changes the parameter used to represent knowledge from simple belief values to sets of evidences. This parameter transformation enables the system to capture uncertain and incomplete information more effectively by representing evidence as sets that can grow and evolve.
Solution Approach 2:
The patent creates a composite knowledge representation by combining rules, evidences, and their mappings into an integrated structure. This composite approach allows the system to handle multiple types of information (certain, uncertain, incomplete) within a unified framework.
3Reliability
If traditional knowledge bases use extension schemes or conditioning rules for reasoning, then the reasoning process is more systematic, but the system produces more anomalies and inconsistencies
Solution Approach 1:
The patent introduces sets of evidences as an intermediary between raw information and conclusions. This intermediary layer allows for more gradual and controlled reasoning, reducing the risk of anomalies by providing additional structure and constraints in the inference process.
Data Source
AI summary
A knowledge-based system under uncertainties and/or incompleteness, referred to as augmented knowledge base (AKB) is provided, including constructing, reasoning, analyzing and applying AKBs by creating objects in the form E→A, where A is a rule in a knowledgebase and E is a set of evidences that supports the rule A. A reasoning scheme under uncertainties and/or incompleteness is provided as augmented reasoning (AR).