Entropy-Based Action Selection for Adaptive Reasoning Models
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Solution Overview
Problem
Computer-based reasoning systems face challenges in adapting to changing contexts, as models trained for specific conditions perform poorly when circumstances change, and combining multiple models leads to inefficiencies due to increased size and computational costs.
Innovation Solution
The technique determines the 'least surprising' action among multiple available models by calculating surprisal and using information gain to select the most appropriate action based on context, allowing for efficient adaptation across various conditions while reducing model size through data splitting and voting mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple computer-based reasoning models are combined to handle different contexts, then adaptability improves, but device complexity and computational costs increase
Solution Approach 1:
An entropy calculation module is introduced as an intermediary component that receives context data and multiple candidate actions, calculates entropy for each action, and selects the action with minimum entropy. This mediator enables the system to adapt to different contexts without requiring multiple separate reasoning models, thus improving adaptability while controlling complexity.
Solution Approach 2:
The patent implements a universal reasoning model that can handle multiple contexts through a single unified architecture. The model uses entropy-based selection to determine the most appropriate action for any given context, eliminating the need for context-specific models and reducing overall system complexity while maintaining versatility.
2Adaptability or versatility
If multiple computer-based reasoning models are combined to handle different contexts, then adaptability improves, but computational costs increase
Solution Approach 1:
The entropy calculation module serves as a computationally efficient intermediary that processes context data and candidate actions to determine the optimal action. By using entropy as a selection criterion rather than executing multiple full reasoning models, the system achieves adaptability across different contexts while significantly reducing computational costs and energy consumption.
3Device complexity
If a single computer-based reasoning model is used for all contexts, then device complexity reduces, but reliability deteriorates
Solution Approach 1:
The system incorporates feedback through entropy calculation, where the entropy value for each candidate action provides information about the quality and appropriateness of that action for the current context. This feedback mechanism enables a single model to reliably select appropriate actions across diverse contexts by evaluating the uncertainty or information content associated with each potential action.
4Reliability
If context-specific computer-based reasoning models are used, then reliability for specific contexts improves, but adaptability to changing contexts worsens
Solution Approach 1:
The patent creates a universal reasoning model that maintains reliability across different contexts through entropy-based action selection. The single model is designed to handle multiple contexts by evaluating candidate actions based on their entropy values, which reflect the appropriateness of each action for the current context, thus achieving both reliability and adaptability.
Data Source
AI summary
Techniques for improved automated selection in computer-based reasoning systems are presented. The techniques include receiving context data for operation of a system, determining two or more candidate actions to take, each from a different computer-based reasoning model, and determining the surprisal of each. The surprisals are then compared and, in some embodiments, the one with the lowest surprisal is chosen. In some embodiments, this chosen action is performed on the system. In some embodiments, the chosen action is passed up a control hierarchy for consideration along with entropy and other factors, and the action chosen at that level is performed on the controlled system.