Safe Control Input Selection Using Mutual Information Search
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Solution Overview
Problem
Existing methods for safe Bayesian Optimization with Gaussian Processes struggle to efficiently explore and exploit safe parameters in computer-controlled systems, often requiring additional hyperparameters and being limited to discrete domains.
Innovation Solution
The proposed method combines safe set expansion with a new acquisition function that maximizes information gain about the safety of parameters, using mutual information and Max-value Entropy Search (MES) to determine the most informative safe parameters for evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SafeOpt method is used to ensure safety constraints are met, then safety is improved, but the method requires additional hyperparameters (Lipschitz constant) and is limited to discrete domains
Solution Approach 1:
The patent changes the fundamental parameters of the safe optimization approach by replacing the Lipschitz continuity assumption with an information-theoretic criterion based on mutual information. This eliminates the need for the Lipschitz constant hyperparameter and extends applicability from discrete to continuous domains, while maintaining safety guarantees through the GP posterior.
Solution Approach 2:
The patent substitutes the mechanical/Lipschitz-based approach of SafeOpt with an information-theoretic approach using mutual information and entropy. This replacement allows the method to work in continuous domains without requiring discretization, while still ensuring safety through the GP posterior model.
2Reliability
If SafeOpt is used to explore safe parameters, then safety is maintained, but data efficiency is reduced due to discretization requirements
Solution Approach 1:
The patent replaces the discretization-based exploration mechanism of SafeOpt with a continuous-domain information-theoretic approach. By using mutual information to select the next evaluation point in continuous space, the method achieves better data efficiency without sacrificing safety guarantees.
Solution Approach 2:
The patent transitions from discrete domain exploration to continuous domain exploration by introducing an information-theoretic dimension (mutual information) as the criterion for selecting evaluation points. This allows efficient exploration in continuous parameter spaces without the computational burden of discretization.
3Productivity
If traditional Bayesian Optimization is used to find optimum, then optimization performance is improved, but constraint violations may occur during exploration
Solution Approach 1:
The patent merges the optimization objective with the safety constraint satisfaction by combining the mutual information criterion (for optimization progress) with the GP-based safe set definition (for constraint satisfaction). This unified approach ensures that evaluation points are selected to maximize information gain while remaining within the safe set, achieving both optimization performance and constraint satisfaction simultaneously.
Data Source
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AI summary
The invention relates to a computer-implemented control method (700) of constrained controlling of a computer-controlled system. The system is controlled according to a control input, which is safe if a constraint quantity resulting from the controlling of the computer-controlled system exceeds a constraint threshold. A current control input is determined based on previous control inputs and corresponding previous noisy measurements. The computer-controlled system is controlled according to the current control input, thereby obtaining a current noisy measurement of the resulting constraint quantity. The current control input is determined based on a mutual information between a first random variable representing the constraint quantity resulting from the current control input and a second random variable indicating whether a further control input is safe and based on the Max-value entropy search (MES) acquisition function.