Small-scale programs exhibit notable compile-time and run-time configurability that grows over time and correlates with size, supporting the value of reducing variability for simpler software.
Proactive self-adaptation under uncertainty: a probabilistic model checking approach,
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Presents hierarchical adaptive refinement to accelerate near-optimal policy synthesis in MDPs up to 1M states with up to 2x speedup over PRISM and formal error bounds.
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Small Yet Configurable: Unveiling Null Variability in Software
Small-scale programs exhibit notable compile-time and run-time configurability that grows over time and correlates with size, supporting the value of reducing variability for simpler software.
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Accelerating Policy Synthesis in Large-Scale MDPs via Hierarchical Adaptive Refinement
Presents hierarchical adaptive refinement to accelerate near-optimal policy synthesis in MDPs up to 1M states with up to 2x speedup over PRISM and formal error bounds.