Reinforcement Learning Semantic Network for Common Sense Reasoning
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Solution Overview
Problem
Existing artificial intelligence and machine learning approaches fail to provide computer systems with sufficient human-like 'common sense' knowledge, limiting their applications across various domains, and semantic-based methods require manual tuning and lack automatic adaptation and scaling.
Innovation Solution
A processor-based method and system that automatically learns semantic-based knowledge, such as categorizations and causal relationships, enabling computer-based systems to exhibit flexible, common sense-based knowledge and reasoning through adaptive systems with fuzzy content networks and auto-learning capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If neural networks are used for machine learning, then the system can process data automatically, but it fails to learn and embody common sense knowledge effectively
Solution Approach 1:
The patent segments the knowledge acquisition process into distinct components: a neural network module for automatic data processing and a semantic network module for common sense knowledge representation. These modules work together but maintain distinct functions, allowing the system to benefit from both automatic processing and reliable knowledge acquisition without one compromising the other.
2Reliability
If semantic-based methods are used, then the system can represent common sense knowledge, but it requires manual tuning and cannot automatically adapt and scale
Solution Approach 1:
The patent implements a self-service mechanism where the semantic network automatically learns from data processed by the neural network. The system uses reinforcement learning to autonomously update semantic relationships and categorizations without requiring manual tuning, thereby maintaining reliable knowledge representation while eliminating the need for complex manual configuration.
Solution Approach 2:
The patent establishes a feedback loop where the neural network's processing results are used to reinforce and update the semantic network. This feedback mechanism allows the system to automatically adapt and scale the common sense knowledge base by continuously learning from new data while maintaining the reliability of semantic representations.
3Adaptability or versatility
If a system automatically learns semantic knowledge, then it can adapt and scale effectively, but it requires complex adaptive systems with fuzzy content networks
Solution Approach 1:
The patent merges two previously separate systems into a unified architecture: the neural network for automatic data processing and the semantic network for knowledge representation. This integration allows the system to achieve automatic adaptation and scaling capabilities while managing complexity through a cohesive design where both components work together synergistically rather than as separate complex systems.
Data Source
AI summary
A reinforcement learning-based semantic method and system interprets content by applying neural networks and then generates and/or updates representations of semantic chains based upon the interpretations. The representations of the semantic chains have associated probabilistic weightings and the semantic chains can comprise causal relationships. Automatic learning occurs as the system assesses the probabilities associated with the semantic chains and focuses its attention accordingly with the intent of increasing its confidence of its inferences. Communications are generated based on the resulting probabilities and a reinforcement learning-based process is then performed with respect to these communications and the probabilities are updated accordingly. A new set of communications is generated based on the updated probabilities. Causal-based explanations for the content of these communications may be provided.