AI Control Training With Integrated Safety Verification
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Solution Overview
Problem
Current AI-based control systems in autonomous systems, such as autonomous vehicles, lack guarantees of safe operation, particularly in safety-critical settings, as they do not adequately integrate safety constraints into the training process, potentially leading to accidents or malfunctions.
Innovation Solution
A safety-aware training method for AI models that iteratively calculates weight values and verifies them against predefined safety constraints using automated reasoning tools like SMT solvers, ensuring the AI model satisfies safety objectives during training, thereby providing a provably safe operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional AI training methods are used to improve model performance and learning speed, then the AI model can achieve higher accuracy and faster training, but the model lacks guarantees of safe operation and may violate safety constraints in safety-critical settings
Solution Approach 1:
The patent applies preliminary action by performing safety verification during the training process itself, rather than after training is complete. The SMT solver is integrated into the training loop to verify safety constraints on weight values as they are being learned, ensuring safety is built into the model from the beginning rather than added as a post-processing step
Solution Approach 2:
The patent introduces an intermediary component - the SMT solver - that acts as a mediator between the AI training process and safety constraints. The SMT solver receives weight values from the training process, verifies them against safety constraints, and provides feedback to guide the training toward safe regions of the parameter space, thus bridging the gap between performance optimization and safety assurance
2Reliability
If safety verification is performed at each iteration of training, then the AI model achieves provably safe operation, but the training time and computational resources increase significantly
Solution Approach 1:
The patent implements feedback by using the SMT solver's verification results to guide subsequent training iterations. When safety constraints are violated, the feedback mechanism adjusts the training process to correct the violations, creating a closed-loop system that continuously ensures safety while progressing toward the learning objective
Solution Approach 2:
The patent applies parameter changes by modifying the weight values of the AI model based on safety verification feedback. When the SMT solver identifies unsafe weight configurations, the training process adjusts these parameters to satisfy safety constraints while maintaining progress toward the learning objective, effectively navigating the parameter space within safe boundaries
Data Source
AI summary
Systems and methods are provided for implementing safety-aware artificial intelligence (AI) that can be used for autonomously controlling systems, such as an autonomous vehicle, in a manner that is proven to satisfy given safety constraints. Additionally, a safety-aware training technique can be applied to learned AI-based models, such as neural networks. The safety-aware training techniques can apply automated reasoning tools (ART) while the AI model is trained, in order to produce a model that is provable safe with respect to the safety constraints. The ART can integrate verification into the training process, and thereby dynamically re-train the model based on the safety verification in a feedback loop approach. The ART can be configured to either verify that the AI model is provably safety, or to provide updates to the training parameters used during to re-train the AI model in instances when the safety verification has failed.