A new efficient algorithm computes optimal conditional reachability probabilities in MDPs without creating hard cyclic reductions, achieving linear time on acyclic cases and substantial speedups on benchmarks from Bayesian networks, probabilistic programs, and runtime monitoring.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Presents a distributionally robust optimization method for sound probabilistic verification of Datalog policies in AI agents that bounds violation risk regardless of predicate correlations.
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Fast Computation of Conditional Probabilities in MDPs and Markov Chain Families
A new efficient algorithm computes optimal conditional reachability probabilities in MDPs without creating hard cyclic reductions, achieving linear time on acyclic cases and substantial speedups on benchmarks from Bayesian networks, probabilistic programs, and runtime monitoring.
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Efficient and Sound Probabilistic Verification for AI Agents
Presents a distributionally robust optimization method for sound probabilistic verification of Datalog policies in AI agents that bounds violation risk regardless of predicate correlations.