{"paper":{"title":"Nonapproximability Results for Partially Observable Markov Decision Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"C. Lusena, J. Goldsmith, M. Mundhenk","submitted_at":"2011-06-01T16:37:53Z","abstract_excerpt":"We show that for several variations of partially observable    Markov decision processes, polynomial-time algorithms for finding    control policies are unlikely to or simply don't have guarantees of    finding policies within a constant factor or a constant summand of    optimal.  Here \"unlikely\" means \"unless some complexity classes    collapse,\" where the collapses considered are P=NP, P=PSPACE, or    P=EXP.  Until or unless these collapses are shown to hold, any    control-policy designer must choose between such performance    guarantees and efficient computation."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1106.0242","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}