A Local Agentic AI Framework to Generate Edge Case Scenarios for Safety Assurance
[Sztuczna inteligencja] Autorzy: Victoria J. Hodge, Colin Paterson, Rob Alexander
Generating edge case scenarios is crucial for training, testing and safety assuring autonomous systems. This identifies hazardous failures to increase system reliability and robustness, ensures the system can still function safely on the boundaries of its operational design domain and learns the expected system behaviour for these edge cases. The rapid advancement in the generation and analysis capabilities of language models (LMs) has expanded their applicability and recent work has investigated LM scenario generation. However, many recent approaches rely on human-in-the-loop or rule-based strategies. This restricts the LM's ability to generate diverse and edge case scenarios; the long tail of the distribution. Our work uses local LMs in a laptop-based agentic AI framework for adaptive scenario generation. This is cheap, fully-controlled, ensures data privacy and security. It uses thin…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja