{"paper":{"title":"Value-Function Approximations for Partially Observable Markov Decision Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"M. Hauskrecht","submitted_at":"2011-06-01T16:24:43Z","abstract_excerpt":"Partially observable Markov decision processes (POMDPs)    provide an elegant mathematical framework for modeling complex    decision and planning problems in stochastic domains in which states    of the system are observable only indirectly, via a set of imperfect    or noisy observations. The modeling advantage of POMDPs, however,    comes at a price -- exact methods for solving them are computationally    very expensive and thus applicable in practice only to very simple    problems. We focus on efficient approximation (heuristic) methods that    attempt to alleviate the computational probl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1106.0234","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"}