Agent Model Building Program Sensitivity Penalty
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Solution Overview
Problem
Existing techniques face difficulties in evaluating conformity to human psychology in agent model building, leading to challenges in improving reproducibility under unknown conditions and measures, as they cannot effectively calculate penalties related to human psychological laws.
Innovation Solution
An agent model building program that selects pairs of samples, acquires output results, and adjusts neural network parameters based on a sensitivity function to ensure training errors and penalties meet predetermined conditions, thereby improving reproducibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a neural network is trained inductively from data without sufficient domain knowledge, then the model can be built without domain knowledge, but the model does not always behave appropriately under unknown circumstances and measures
Solution Approach 1:
The patent implements feedback by calculating penalties based on sensitivity function violations and using these penalties to adjust neural network parameters during training. The system continuously monitors whether the neural network's output satisfies sensitivity conditions and adjusts parameters to minimize penalty, creating a closed-loop training process that ensures domain knowledge consistency while maintaining inductive learning capabilities
Solution Approach 2:
The patent changes parameters by adjusting neural network parameters based on calculated penalties from sensitivity function violations. The system modifies network parameters iteratively to minimize the penalty term in the loss function, thereby transforming the network's behavior to conform to domain knowledge requirements while maintaining the flexibility of inductive learning
2Reliability
If a penalty term is added to the loss function to enforce domain knowledge, then the neural network becomes consistent with domain knowledge, but the complexity of designing an appropriate penalty term increases
Solution Approach 1:
The patent segments the penalty calculation into distinct components: selecting sample pairs, calculating neural network outputs, evaluating sensitivity function violations, and computing penalties. This segmentation breaks down the complex penalty term design into manageable steps, where each component has a specific function and can be independently adjusted or replaced based on different domain knowledge requirements
3Measurement precision
If existing techniques evaluate physical laws from predicted value structure, then physical law conformity can be checked, but human psychology conformity cannot be evaluated
Solution Approach 1:
The patent creates a universal sensitivity function framework that can evaluate different types of domain knowledge across various domains. The sensitivity function takes a stimulus variable and response variable as inputs and can be configured to represent different laws (physical, psychological, economic, etc.). This universal structure allows the same penalty calculation mechanism to enforce diverse domain knowledge requirements, making the system adaptable to different application areas
Data Source
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AI summary
An agent model building program that causes at least one computer to execute a process, the process includes selecting a pair of samples among samples included in a data set, each of the pair of samples having a value other than stimulus variables related to an input to an agent; acquiring a first output result of the data set by inputting the data set to a neural network; acquiring a first penalty based on whether the value conform to a sensitivity function; and adjusting a parameter of the neural network until a training error based on the first output result and the first penalty satisfy a predetermined condition.