Autonomous Plan Acceptability Criteria via Rule Distillation
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Solution Overview
Problem
Current approaches for generating autonomous systems plans using Deep Reinforcement Learning (DRL) do not guarantee safety and task accuracy, as they lack flexibility and adaptability to task and environment conditions, and rely on manually tuned rules that may not capture all limitations or constraints.
Innovation Solution
A system that generates formally verifiable acceptability criteria for autonomous systems plans, using a neural encoding scheme to distill rules from historical data and adapt them to specific tasks and environments through a domain adaptation mechanism, ensuring safe and accurate execution by filtering control commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually tuned rules are used to constrain autonomous systems, then safety can be partially ensured, but the system lacks flexibility and adaptability to task and environment conditions
Solution Approach 1:
The patent replaces manually tuned mechanical rule-based systems with a neural network-based acceptability criteria generator. The neural network learns safety criteria from historical data and task demonstrations, substituting rigid manual rules with an adaptive intelligent system that can generalize to new situations while maintaining safety guarantees through formal verification methods.
Solution Approach 2:
The system changes the parameter representation from fixed manual rules to learned neural network parameters. By training the neural network on historical safe trajectories and task demonstrations, the system dynamically adjusts its safety criteria parameters based on the specific task and environment, enabling both safety and adaptability.
2Adaptability or versatility
If Deep Reinforcement Learning is used to generate autonomous plans, then the system can learn complex behaviors, but safety and task accuracy are not guaranteed
Solution Approach 1:
The patent introduces an intermediary acceptability criteria generator that acts as a mediator between the DRL planner and the autonomous system execution. The neural network generates acceptability criteria that filter and validate DRL-generated plans, ensuring safety and task accuracy while preserving the learning capability of the underlying DRL system.
Solution Approach 2:
The system performs preliminary generation of acceptability criteria before executing autonomous plans. By pre-training the neural network on historical safe data and generating acceptance criteria in advance, the system ensures that safety checks are already in place before the DRL system generates its plans, preventing unsafe behaviors from being executed.
3Device complexity
If manually tuned rules are used, then the system structure is simple, but the rules may not capture all limitations or constraints
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically generates its own safety constraints through the neural network trained on historical data. Instead of relying on manual rule creation, the system serves itself by learning comprehensive constraints from operational data, capturing limitations and constraints that may be too complex or numerous for manual specification.
Data Source
AI summary
Apparatus, systems, articles of manufacture, and methods to generate acceptability criteria for autonomous systems plans are disclosed. An example apparatus includes a data compiler to compile data generated by the autonomous system into an autonomous system task dataset, a data encoder to encode the dataset for input into a rule distillation neural network architecture, a model trainer to train the rule distillation neural network architecture, an adaptor to adapt the trained rule distillation neural network architecture to a new input data domain using the autonomous system task dataset, a verifier to generate formally verified acceptability criteria, and an inferer to evaluate a control command, the evaluation resulting in an acceptance or rejection of the command.