Algorithms for Deciding the Safety of States in Fully Observable Non-deterministic Problems: Technical Report
arXiv:2603.15282v2 Announce Type: replace Abstract: Learned action policies are increasingly popular in sequential decision-making, but suffer from a lack of safety guarantees. Recent work introduced a pipeline for testing the safety of such policies under initial-state and action-outcome non-determinism. At the pipeline's core, is the problem of deciding whether a state is safe (a safe policy exists from the state) and finding faults, which are state-action pairs that transition from a safe state to an unsafe one. Their most effective algorithm for deciding safety, TarjanSafe, is effective on
The increasing deployment of learned action policies in real-world sequential decision-making systems necessitates robust methods for ensuring their safety, driving current research in this area.
Ensuring the safety of AI agents, particularly in non-deterministic environments, is critical for their widespread adoption and to prevent unintended consequences or failures.
This technical report advances the algorithmic understanding for proactively identifying and preventing unsafe behaviors in AI policies, moving towards more reliable autonomous systems.
- · AI safety researchers
- · Developers of autonomous systems
- · Industries deploying AI in critical applications
- · Developers relying solely on empirical testing for AI safety
Improved theoretical foundations and practical algorithms for verifying the safety of AI policies will emerge.
Safer and more dependable AI agents could accelerate their deployment in sensitive or high-stakes environments.
Established safety verification techniques could become a standard requirement for regulatory approval of advanced AI systems.
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Read at arXiv cs.AI