Finite Reliability Representations: Noise-Calibrated Belief-Space Covers for Reliable Decision-Making
arXiv:2607.04019v1 Announce Type: cross Abstract: Physical sensing and actuation noise floors should inform how much belief resolution a decision-making system can reliably use. We introduce Finite Reliability Representations (FRR), a framework for covering belief spaces by reliability cells: regions within which the optimal action-value function Q*(b,u) varies by at most a tolerance epsilon, uniformly over actions. The framework is formulated on beliefs rather than states and uses a cover rather than an equivalence quotient, because approximate decision-closeness is not transitive in general.
The increasing complexity and autonomy of AI systems necessitate more reliable decision-making under uncertainty, which this framework directly addresses.
This research provides a foundational approach for building more robust and trustworthy autonomous systems by explicitly accounting for limits in sensing and actuation, crucial for widespread adoption.
The explicit incorporation of noise-calibrated reliability into AI decision-making shifts the paradigm from theoretical optimality to practical, robust performance in real-world messy environments.
- · Autonomous systems developers
- · Robotics industry
- · AI safety researchers
- · Industrial automation
- · AI systems with brittle decision logic
- · Applications requiring high precision in noisy environments without robust contr
More reliable and deployable AI systems across various domains, particularly those involving physical interaction.
Reduced incidence of failures and unpredictable behavior in autonomous robots and decision-making agents, fostering greater public trust.
Accelerated integration of AI into critical infrastructure and high-stakes applications due to enhanced reliability assurances.
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Read at arXiv cs.AI