Deep Adaptive Semantic Logic Network for Explainable AI Reasoning
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Solution Overview
Problem
Current artificial intelligence systems either rely on narrow knowledge-based reasoning or data-driven learning, often requiring extensive data sets and labor-intensive encoding, and struggle to express scientific knowledge in declarative logical forms effectively.
Innovation Solution
An artificial intelligence engine that uses a deep adaptive semantic logic network to create machine learning models by encoding human knowledge and reasoning into rules, adapting these rules through vector modifications and statistical conclusions to optimize interpretations and provide explainable results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If knowledge-based reasoning systems are used, then reasoning capability is improved, but scope and adaptability deteriorate
Solution Approach 1:
The patent merges knowledge-based reasoning systems with data-driven machine learning systems into a unified hybrid architecture. The neural network learns from data while incorporating domain knowledge through custom loss functions and constraints, enabling the system to maintain strong reasoning capabilities while significantly improving adaptability and scope across different domains and datasets.
Solution Approach 2:
The system creates a composite AI architecture that combines symbolic knowledge representation with subsymbolic neural network learning. This composite approach integrates the strengths of both paradigms: the interpretability and reasoning of knowledge-based systems with the pattern recognition and adaptability of data-driven learning, resulting in a more versatile and robust system.
2Adaptability or versatility
If data-driven learning is used, then learning capability is improved, but data requirements and customization effort increase
Solution Approach 1:
The system performs preliminary action by pre-incorporating domain knowledge, rules, and constraints into the neural network architecture before training begins. This upfront integration of knowledge allows the model to learn effectively from smaller datasets, as the pre-encoded knowledge provides a strong prior that guides learning and reduces the amount of training data needed.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting neural network parameters during training based on both data patterns and knowledge-based constraints. The custom loss functions modify parameter updates to ensure consistency with domain knowledge, enabling effective learning with reduced data requirements while maintaining adaptability.
3Measurement precision
If machine reasoning techniques are used, then reasoning precision is improved, but terminology requirements and complexity increase
Solution Approach 1:
The patent substitutes rigid mechanical-style rule-based reasoning with a flexible neural network system that incorporates knowledge through soft constraints and loss functions. This replacement maintains reasoning precision while reducing terminology requirements, as the neural network can handle variability in language and representation without requiring exact precision in terminology.
Solution Approach 2:
The system introduces dynamics by making the reasoning process adaptive rather than static. The neural network dynamically adjusts its reasoning based on learned patterns while maintaining consistency with domain knowledge, allowing the system to handle diverse terminology and representations without requiring fixed, precise terminology definitions.
4Reliability
If custom statistical models are used, then expertise encoding is improved, but labor intensity and scope limitation increase
Solution Approach 1:
The patent enables self-service by allowing the neural network to automatically learn and encode expertise from data while incorporating domain knowledge through automated constraint integration. This reduces labor intensity compared to manual encoding of expertise into custom statistical models, as the system performs much of the expertise acquisition and integration automatically during training.
Solution Approach 2:
The system achieves universality by creating a general-purpose neural network framework that can handle multiple domains and expertise types through a unified architecture. This multi-functional approach reduces the need to build separate custom statistical models for different domains, significantly reducing overall labor intensity while maintaining high-quality expertise encoding across diverse applications.
Data Source
AI summary
An artificial intelligence engine that has two or more modules cooperating with each other in order to create one or more machine learning models that use an adaptive semantic learning for knowledge representations and reasoning. The modules cause encoding the representations and reasoning from one or more sources in a particular field with terminology used by one or more human sources in that field into a set of rules that act as constraints and that are graphed into a neural network understandable by a first machine learning model, and then ii) adapting an interpretation of that set of encoded rules. The understanding of that set of encoded rules is adapted by i) allowing for semantically similar terms and ii) by conclusions derived from training data, to create an understanding of that set of encoded rules utilized by the machine learning model and the AI engine.