A POMDP decomposition method scales solving of the Sensor Selection Problem and Positional Observability Problem by 3 and 5 orders of magnitude in instance size and runtime.
Permissive Finite-State Controllers of POMDPs using Parameter Synthesis
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abstract
We study finite-state controllers (FSCs) for partially observable Markov decision processes (POMDPs) that are provably correct with respect to given specifications. The key insight is that computing (randomised) FSCs on POMDPs is equivalent to - and computationally as hard as - synthesis for parametric Markov chains (pMCs). This correspondence allows to use tools for parameter synthesis in pMCs to compute correct-by-construction FSCs on POMDPs for a variety of specifications. Our experimental evaluation shows comparable performance to well-known POMDP solvers.
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2026 1verdicts
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Scaling Observation-aware Planning in Uncertain Domains
A POMDP decomposition method scales solving of the Sensor Selection Problem and Positional Observability Problem by 3 and 5 orders of magnitude in instance size and runtime.