Entropy-Based Action Selection for Context-Adaptive Reasoning
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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 contexts shift, and combining multiple models leads to inefficiencies due to increased size and computational costs.
Innovation Solution
The approach determines the 'least surprising' action among multiple available models by calculating surprisal and using information gain to select the most appropriate action, allowing for efficient context adaptation while reducing model size through feature or label 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:
The patent segments the reasoning system into multiple specialized models, each trained for specific contexts (e.g., sunny day highway driving vs. rainy day highway driving). Instead of one large general model, the system divides functionality into context-specific segments that can be selectively activated based on current conditions, reducing overall complexity while maintaining adaptability.
Solution Approach 2:
The system dynamically selects which reasoning model to use based on real-time context assessment. The model selection is not static but adapts continuously as contexts change, allowing the system to optimize performance for current conditions without maintaining all models simultaneously active, thus managing complexity while preserving 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:
By segmenting into specialized models, the system avoids running a single large general-purpose model for all contexts. Each specialized model is more computationally efficient for its specific domain, reducing energy consumption while maintaining the ability to handle diverse contexts through selective model activation.
Solution Approach 2:
The system applies partial action by activating only the necessary reasoning model for the current context rather than running all models simultaneously. This selective activation reduces computational energy consumption while still providing comprehensive context handling capability when needed.
3Device complexity
If a single reasoning model is used for all contexts, then device complexity reduces, but reliability deteriorates when contexts change
Solution Approach 1:
Each reasoning model is optimized for specific local contexts (e.g., sunny day highway driving) rather than attempting to handle all contexts equally. This local specialization ensures high reliability within each context domain while the overall system maintains simplicity through modular model structures that can be independently validated and trusted for their specific purposes.
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.