Tabular Policy Models for Interpretable Clinical Task Execution
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Solution Overview
Problem
Existing action models for computer control in clinical applications face challenges such as data inefficiency, lack of interpretability, hallucinations, and model regression, making them unsuitable for reliable and efficient task execution.
Innovation Solution
Implementing a tabular policy action model that maps states to actions using a table-based approach, avoiding neural networks and addressing issues of data efficiency, interpretability, and model regression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks are used for action models, then adaptability and learning capability are improved, but data efficiency deteriorates and interpretability is lost
Solution Approach 1:
The patent replaces neural networks with a tabular policy system that uses explicit rule-based decision-making. Instead of using complex neural network architectures that require large datasets for training, the system employs a table-based approach where actions are determined by looking up pre-defined rules based on current state representations, eliminating the need for extensive training data while maintaining decision-making capability.
Solution Approach 2:
The patent creates a simplified copy of the decision-making process by representing policies as explicit tables that map states to actions. This tabular representation is a simplified version of neural network decision-making that captures the essential logic without requiring the complex computational infrastructure and large datasets that neural networks need.
2Adaptability or versatility
If neural networks are used for action models, then adaptability and learning capability are improved, but interpretability deteriorates
Solution Approach 1:
The patent replaces the black-box neural network system with an explicit rule-based system where the decision-making logic is stored in interpretable tables. Each entry in the policy table explicitly shows the state conditions and corresponding actions, making the system fully transparent and explainable while maintaining the ability to learn from demonstrations through the demonstration-based policy generation process.
3Adaptability or versatility
If neural networks are used for action models, then adaptability is improved, but reliability deteriorates due to hallucinations
Solution Approach 1:
The patent replaces neural network-based action models with a tabular policy system that determines actions through explicit rule lookup. This eliminates the hallucination problem inherent in neural networks by ensuring that actions are always selected from pre-defined, validated rules in the policy table, thereby improving reliability while maintaining adaptability through the demonstration-based learning mechanism.
Solution Approach 2:
The system incorporates feedback through the demonstration-based policy generation process, where successful demonstrations are used to update and refine the policy table. This feedback mechanism ensures that only verified, successful actions are added to the policy, preventing hallucinations while enabling continuous improvement and adaptation to new tasks.
4Adaptability or versatility
If neural networks are used for action models, then adaptability is improved, but stability deteriorates due to model regression
Solution Approach 1:
The patent replaces neural networks with a tabular policy system that stores decisions as explicit, immutable rules. This eliminates model regression because the policy table does not undergo the weight optimization processes that cause neural networks to forget previously learned information. The system maintains stable, consistent decision-making while adapting to new tasks through the addition of new rules to the table.
5Quantity of substance
If tabular policy action models are used, then data efficiency and interpretability are improved, but device complexity increases
Solution Approach 1:
The patent segments the complex decision-making process into discrete, manageable components by organizing policies as tables with specific state-action mappings. This segmentation makes the system more interpretable and easier to debug compared to neural networks, while the modular table structure can be efficiently managed through standard computing operations without requiring complex hardware infrastructure.
Data Source
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AI summary
In some aspects, the present disclosure provides a computer-implemented system comprising: a digital processing device comprising: at least one processor, an operating system configured to perform executable instructions, a memory, and a computer program including instructions executable by the digital processing device to perform a computer task by a user expressing to the computer-implemented system, and wherein the computer-implemented system is configured to perform the task by generating and executing a computer-executable program.