@inproceedings{21722,
  abstract     = {Partially observable Markov decision processes (POMDPs) are a central model for uncertainty in sequential decision making. The most basic objective is the reachability objective, where a target set must be eventually visited, and the more general parity objectives can model all omega-regular specifications. For such objectives, the computational analysis problems are the following: (a) qualitative analysis that asks whether the objective can be satisfied with probability 1 (almost-sure winning) or probability arbitrarily close to 1 (limit-sure winning); and (b) quantitative analysis that asks for the approximation of the optimal probability of satisfying the objective. For general POMDPs, almost-sure analysis for reachability objectives is EXPTIME-complete, but limit-sure and quantitative analyses for reachability objectives are undecidable; almost-sure, limit-sure, and quantitative analyses for parity objectives are all undecidable. A special class of POMDPs, called revealing POMDPs, has been studied recently in several works, and for this subclass the almost-sure analysis for parity objectives was shown to be EXPTIME-complete. In this work, we show that for revealing POMDPs the limit-sure analysis for parity objectives is EXPTIME-complete, and even the quantitative analysis for parity objectives can be achieved in EXPTIME.},
  author       = {Asadi, Ali and Chatterjee, Krishnendu and Lurie, David and Saona Urmeneta, Raimundo J},
  booktitle    = {Proceedings of the AAAI Conference on Artificial Intelligence},
  issn         = {2374-3468},
  location     = {Singapore, Singapore},
  number       = {43},
  pages        = {36146--36154},
  publisher    = {Association for the Advancement of Artificial Intelligence},
  title        = {{Revealing POMDPs: Qualitative and quantitative analysis for parity objectives}},
  doi          = {10.1609/aaai.v40i43.40932},
  volume       = {40},
  year         = {2026},
}