State Observer Linear Transition Control for Neural Network Generalization
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Solution Overview
Problem
Current neural networks face challenges in effectively classifying input patterns and achieving generalization for unseen inputs, particularly in complex environments, as they lack efficient methods to determine skills and goals that lead to linear state transitions.
Innovation Solution
An electronic device equipped with a state observer, processors, and a controller, utilizing machine learning models to determine skills and goals, and perform actions that cause linear state transitions, by sensing environmental changes, updating models based on rewards, and maintaining skills and goals over predetermined times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks use traditional classification algorithms, then they can classify input patterns into specific groups, but they lack generalization ability for unseen inputs in complex environments
Solution Approach 1:
The patent segments the complex decision-making process into distinct components: skill determination module, goal determination module, and action determination module. Each module handles a specific aspect of the problem, allowing the system to process complex environments through modular processing rather than attempting monolithic classification, thereby improving generalization while managing complexity.
Solution Approach 2:
The patent introduces a hierarchical dimension to the decision-making process by adding goal and skill abstraction layers above the basic action level. This dimensional transformation allows the system to generalize across unseen inputs by reasoning about skills and goals rather than relying solely on pattern matching, thus enhancing adaptability without proportionally increasing complexity.
2Reliability
If neural networks attempt to handle complex environments with traditional methods, then they may achieve basic classification, but they fail to determine skills and goals that lead to linear state transitions
Solution Approach 1:
The patent introduces skill vectors and goal vectors as intermediary representations between the raw state and the final action. These intermediaries decompose the complex state transition problem into manageable components: determining the appropriate skill, selecting the goal, and then executing the action. This intermediary layer enables predictable linear state transitions while keeping individual model components relatively simple.
Solution Approach 2:
The system performs preliminary determination of skills and goals before executing actions. By pre-determining the skill vector and goal vector based on the current state, the system prepares a structured plan that guides subsequent actions, ensuring linear and predictable state transitions toward the desired goal without requiring complex real-time decision-making.
3Ease of operation
If the system determines skills and goals for every state, then it can achieve precise control, but it increases computational complexity and processing time
Solution Approach 1:
The patent applies partial action by determining skills and goals only when necessary rather than for every single state transition. The skill determination model and goal determination model are invoked selectively based on the situation, allowing the system to maintain precise control where needed while reducing computational overhead in scenarios where simpler responses suffice, thus balancing control precision with processing efficiency.
Data Source
AI summary
An electronic device includes: a state observer configured to observe a state of the electronic device according to an environment interactable with the electronic device; one or more processors configured to: determine a skill based on the observed state; determine a goal based on the determined skill and the observed state; and determine, based on the state and the determined goal, an action causing a linear state transition of the electronic device in a direction toward the determined goal in a state space; and a controller configured to control an operation of the electronic device based on the determined action.