{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:GFZXVTHQOGJ2L2E6Q2IP53H3KO","short_pith_number":"pith:GFZXVTHQ","schema_version":"1.0","canonical_sha256":"31737accf07193a5e89e8690feecfb53b8201bb6c59f88c822a09f01f37da04e","source":{"kind":"arxiv","id":"1203.3477","version":1},"attestation_state":"computed","paper":{"title":"A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Tom Erez, William D. Smart","submitted_at":"2012-03-15T11:17:56Z","abstract_excerpt":"Partially-Observable Markov Decision Processes (POMDPs) are typically solved by finding an approximate global solution to a corresponding belief-MDP. In this paper, we offer a new planning algorithm for POMDPs with continuous state, action and observation spaces. Since such domains have an inherent notion of locality, we can find an approximate solution using local optimization methods. We parameterize the belief distribution as a Gaussian mixture, and use the Extended Kalman Filter (EKF) to approximate the belief update. Since the EKF is a first-order filter, we can marginalize over the obser"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1203.3477","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-03-15T11:17:56Z","cross_cats_sorted":[],"title_canon_sha256":"cc4a22cb4fca12ec019733298a214e3730940f66a24e5a2873d1525dfb983ffd","abstract_canon_sha256":"dd37d7cd47def97a1d123b70f20d81b5bd875db1ae27c2754dc162e572011618"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:00:00.429477Z","signature_b64":"txVVA8rLjH8d6rwKRk7Vs+uNzW1/0sokj/JTZti+Gux97IGZIlFCo41dribqiej0+Oh88iu9D9S3eEsuPmb4Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31737accf07193a5e89e8690feecfb53b8201bb6c59f88c822a09f01f37da04e","last_reissued_at":"2026-05-18T04:00:00.428786Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:00:00.428786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Tom Erez, William D. Smart","submitted_at":"2012-03-15T11:17:56Z","abstract_excerpt":"Partially-Observable Markov Decision Processes (POMDPs) are typically solved by finding an approximate global solution to a corresponding belief-MDP. In this paper, we offer a new planning algorithm for POMDPs with continuous state, action and observation spaces. Since such domains have an inherent notion of locality, we can find an approximate solution using local optimization methods. We parameterize the belief distribution as a Gaussian mixture, and use the Extended Kalman Filter (EKF) to approximate the belief update. Since the EKF is a first-order filter, we can marginalize over the obser"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1203.3477","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1203.3477","created_at":"2026-05-18T04:00:00.428885+00:00"},{"alias_kind":"arxiv_version","alias_value":"1203.3477v1","created_at":"2026-05-18T04:00:00.428885+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1203.3477","created_at":"2026-05-18T04:00:00.428885+00:00"},{"alias_kind":"pith_short_12","alias_value":"GFZXVTHQOGJ2","created_at":"2026-05-18T12:27:06.952714+00:00"},{"alias_kind":"pith_short_16","alias_value":"GFZXVTHQOGJ2L2E6","created_at":"2026-05-18T12:27:06.952714+00:00"},{"alias_kind":"pith_short_8","alias_value":"GFZXVTHQ","created_at":"2026-05-18T12:27:06.952714+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GFZXVTHQOGJ2L2E6Q2IP53H3KO","json":"https://pith.science/pith/GFZXVTHQOGJ2L2E6Q2IP53H3KO.json","graph_json":"https://pith.science/api/pith-number/GFZXVTHQOGJ2L2E6Q2IP53H3KO/graph.json","events_json":"https://pith.science/api/pith-number/GFZXVTHQOGJ2L2E6Q2IP53H3KO/events.json","paper":"https://pith.science/paper/GFZXVTHQ"},"agent_actions":{"view_html":"https://pith.science/pith/GFZXVTHQOGJ2L2E6Q2IP53H3KO","download_json":"https://pith.science/pith/GFZXVTHQOGJ2L2E6Q2IP53H3KO.json","view_paper":"https://pith.science/paper/GFZXVTHQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1203.3477&json=true","fetch_graph":"https://pith.science/api/pith-number/GFZXVTHQOGJ2L2E6Q2IP53H3KO/graph.json","fetch_events":"https://pith.science/api/pith-number/GFZXVTHQOGJ2L2E6Q2IP53H3KO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GFZXVTHQOGJ2L2E6Q2IP53H3KO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GFZXVTHQOGJ2L2E6Q2IP53H3KO/action/storage_attestation","attest_author":"https://pith.science/pith/GFZXVTHQOGJ2L2E6Q2IP53H3KO/action/author_attestation","sign_citation":"https://pith.science/pith/GFZXVTHQOGJ2L2E6Q2IP53H3KO/action/citation_signature","submit_replication":"https://pith.science/pith/GFZXVTHQOGJ2L2E6Q2IP53H3KO/action/replication_record"}},"created_at":"2026-05-18T04:00:00.428885+00:00","updated_at":"2026-05-18T04:00:00.428885+00:00"}