{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:BO7SMU25MZ4SB4QJRHZUFS6UUN","short_pith_number":"pith:BO7SMU25","schema_version":"1.0","canonical_sha256":"0bbf26535d667920f20989f342cbd4a351b72edf3c66a6894d46443563362a6e","source":{"kind":"arxiv","id":"1203.3523","version":1},"attestation_state":"computed","paper":{"title":"Risk Sensitive Path Integral Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.SY","authors_text":"Bart van den Broek, Hilbert Kappen, Wim Wiegerinck","submitted_at":"2012-03-15T11:17:56Z","abstract_excerpt":"Recently path integral methods have been developed for stochastic optimal control for a wide class of models with non-linear dynamics in continuous space-time. Path integral methods find the control that minimizes the expected cost-to-go. In this paper we show that under the same assumptions, path integral methods generalize directly to risk sensitive stochastic optimal control. Here the method minimizes in expectation an exponentially weighted cost-to-go. Depending on the exponential weight, risk seeking or risk averse behaviour is obtained. We demonstrate the approach on risk sensitive stoch"},"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":"1203.3523","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2012-03-15T11:17:56Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"a82c4c8d958bdcdb16e1dbe3dd6c26c5547601b2284cf48f72f1d1d2c909f183","abstract_canon_sha256":"ec4fe581e2e0f7569c5500d1c693ec8990242fe64c489e2b28c573a9bab13d2a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:59:59.888124Z","signature_b64":"3WzegqmBDf4nNnVcHfdyn01A8ucPorxHmQdLMfU8VI7OdMZQODRdR9h82sKzWYpBYv9ZyVkZvNqu4IL33iztBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0bbf26535d667920f20989f342cbd4a351b72edf3c66a6894d46443563362a6e","last_reissued_at":"2026-05-18T03:59:59.886840Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:59:59.886840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Risk Sensitive Path Integral Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.SY","authors_text":"Bart van den Broek, Hilbert Kappen, Wim Wiegerinck","submitted_at":"2012-03-15T11:17:56Z","abstract_excerpt":"Recently path integral methods have been developed for stochastic optimal control for a wide class of models with non-linear dynamics in continuous space-time. Path integral methods find the control that minimizes the expected cost-to-go. In this paper we show that under the same assumptions, path integral methods generalize directly to risk sensitive stochastic optimal control. Here the method minimizes in expectation an exponentially weighted cost-to-go. Depending on the exponential weight, risk seeking or risk averse behaviour is obtained. We demonstrate the approach on risk sensitive stoch"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1203.3523","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":"1203.3523","created_at":"2026-05-18T03:59:59.886944+00:00"},{"alias_kind":"arxiv_version","alias_value":"1203.3523v1","created_at":"2026-05-18T03:59:59.886944+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1203.3523","created_at":"2026-05-18T03:59:59.886944+00:00"},{"alias_kind":"pith_short_12","alias_value":"BO7SMU25MZ4S","created_at":"2026-05-18T12:27:01.376967+00:00"},{"alias_kind":"pith_short_16","alias_value":"BO7SMU25MZ4SB4QJ","created_at":"2026-05-18T12:27:01.376967+00:00"},{"alias_kind":"pith_short_8","alias_value":"BO7SMU25","created_at":"2026-05-18T12:27:01.376967+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.11917","citing_title":"A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression","ref_index":52,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BO7SMU25MZ4SB4QJRHZUFS6UUN","json":"https://pith.science/pith/BO7SMU25MZ4SB4QJRHZUFS6UUN.json","graph_json":"https://pith.science/api/pith-number/BO7SMU25MZ4SB4QJRHZUFS6UUN/graph.json","events_json":"https://pith.science/api/pith-number/BO7SMU25MZ4SB4QJRHZUFS6UUN/events.json","paper":"https://pith.science/paper/BO7SMU25"},"agent_actions":{"view_html":"https://pith.science/pith/BO7SMU25MZ4SB4QJRHZUFS6UUN","download_json":"https://pith.science/pith/BO7SMU25MZ4SB4QJRHZUFS6UUN.json","view_paper":"https://pith.science/paper/BO7SMU25","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1203.3523&json=true","fetch_graph":"https://pith.science/api/pith-number/BO7SMU25MZ4SB4QJRHZUFS6UUN/graph.json","fetch_events":"https://pith.science/api/pith-number/BO7SMU25MZ4SB4QJRHZUFS6UUN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BO7SMU25MZ4SB4QJRHZUFS6UUN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BO7SMU25MZ4SB4QJRHZUFS6UUN/action/storage_attestation","attest_author":"https://pith.science/pith/BO7SMU25MZ4SB4QJRHZUFS6UUN/action/author_attestation","sign_citation":"https://pith.science/pith/BO7SMU25MZ4SB4QJRHZUFS6UUN/action/citation_signature","submit_replication":"https://pith.science/pith/BO7SMU25MZ4SB4QJRHZUFS6UUN/action/replication_record"}},"created_at":"2026-05-18T03:59:59.886944+00:00","updated_at":"2026-05-18T03:59:59.886944+00:00"}