AI RESEARCH
Computing the Reachability Value of Posterior-Deterministic POMDPs
arXiv CS.AI
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ArXi:2602.07473v2 Announce Type: replace Partially observable Marko decision processes (POMDPs) are a fundamental model for sequential decision-making under uncertainty. However, many verification and synthesis problems for POMDPs are undecidable or intractable. Most prominently, the seminal result of Madani states that there is no algorithm that, given a POMDP and a set of target states, can compute the maximal probability of reaching the target states, or even approximate it up to a non-trivial constant.