Autonomous System Compliance Testing with Bayesian Scenario Generation
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Solution Overview
Problem
Current testing methods for autonomous devices are time-consuming and resource-intensive, and lack transparency in ensuring safe operation, making it difficult to provide proof of compliance with legislative frameworks.
Innovation Solution
A method and device utilizing machine learning and Bayesian networks to efficiently determine test cases by correlating actions and contexts with principles, enabling transparent validation and certification of autonomous systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute-force testing methods are used to ensure apparatus operates as expected, then testing completeness is improved, but time consumption and resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-defining principles that describe desired apparatus behavior in advance. These principles are used to generate test cases before actual testing begins, allowing the system to focus only on relevant test scenarios rather than exhaustively testing all possible situations. This resolves the contradiction by ensuring testing completeness through principled coverage while reducing time consumption by avoiding redundant tests.
Solution Approach 2:
The patent changes the parameter of test case generation from exhaustive enumeration to principle-based selection. By transforming the testing approach from checking all possible state combinations to generating test cases based on predefined behavioral principles, the system achieves adequate coverage with significantly reduced testing time and resources.
2Reliability
If brute-force testing methods are used to ensure apparatus operates as expected, then testing coverage is improved, but resource consumption increases significantly
Solution Approach 1:
The patent pre-establishes principles describing desired apparatus behavior before testing begins. These principles guide test case generation to focus only on scenarios that verify principle compliance, eliminating wasted resources on redundant or irrelevant test cases while maintaining comprehensive coverage of critical operational aspects.
Solution Approach 2:
The patent transforms the testing resource allocation from uniform exhaustive testing to targeted principle-based testing. By changing the parameter of test selection from random or systematic enumeration to principle-driven generation, the system achieves effective coverage with optimized resource consumption.
3Reliability
If traditional testing methods are used for validation and certification, then compliance verification is achieved, but transparency and proof of safe operation become difficult to provide
Solution Approach 1:
The patent implements feedback by systematically recording and reporting test results against predefined principles. The testing process provides transparent feedback on which principles are satisfied and how the apparatus behaves relative to expected safe operations. This creates a verifiable record that demonstrates compliance while maintaining transparency about system behavior and limitations.
Data Source
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AI summary
A method and a device (100) for testing, the device (100) comprising a learning arrangement (102) adapted to provide scenarios for test cases and principles to be tested, in particular comprising a digital representation of one or more of a law, an accident report, a log, or human expertise or a combination thereof, wherein the learning arrangement (102) is adapted to determine at least one rule for test case generation from the scenarios and the principles, and wherein a modelling arrangement (104) is adapted to determine, store and/or output a model (110) for test case generation depending on the at least one rule.