{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:U463FE3ZGTXQLUBBNKITCGHWKV","short_pith_number":"pith:U463FE3Z","canonical_record":{"source":{"id":"1704.07183","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-04-24T12:44:38Z","cross_cats_sorted":[],"title_canon_sha256":"8c28c8b17dd7ac2a942b6e6acc2e100d926462a470fae4594df90faaa9f547dc","abstract_canon_sha256":"703e5af52af2861d05dfd504cf0e61ccbff53089aaa1703caab289a69c8c82de"},"schema_version":"1.0"},"canonical_sha256":"a73db2937934ef05d0216a913118f65553ff755631987e8deee5d92e1ba088ae","source":{"kind":"arxiv","id":"1704.07183","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.07183","created_at":"2026-05-18T00:45:53Z"},{"alias_kind":"arxiv_version","alias_value":"1704.07183v1","created_at":"2026-05-18T00:45:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.07183","created_at":"2026-05-18T00:45:53Z"},{"alias_kind":"pith_short_12","alias_value":"U463FE3ZGTXQ","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"U463FE3ZGTXQLUBB","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"U463FE3Z","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:U463FE3ZGTXQLUBBNKITCGHWKV","target":"record","payload":{"canonical_record":{"source":{"id":"1704.07183","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-04-24T12:44:38Z","cross_cats_sorted":[],"title_canon_sha256":"8c28c8b17dd7ac2a942b6e6acc2e100d926462a470fae4594df90faaa9f547dc","abstract_canon_sha256":"703e5af52af2861d05dfd504cf0e61ccbff53089aaa1703caab289a69c8c82de"},"schema_version":"1.0"},"canonical_sha256":"a73db2937934ef05d0216a913118f65553ff755631987e8deee5d92e1ba088ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:53.294688Z","signature_b64":"yYDrw8EH89XYWfPem0V+RjWXPNvpS3vlOlsdgHwlJFARIOvxyzxdVEoV3cW/6UK0gDGYxbLO2lKO3GkeysDaBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a73db2937934ef05d0216a913118f65553ff755631987e8deee5d92e1ba088ae","last_reissued_at":"2026-05-18T00:45:53.294065Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:53.294065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.07183","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:45:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gdzDmZ6difHD4REhlS9tAiCAJDDqjYf38ERabIgMHn2tYWVuGK7aGgdhPBwWmYqx6q8+7hAsDFuJcEwfu4TABg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T14:55:56.588646Z"},"content_sha256":"cf3ba84edc49382e179985f53518e4ba455c8b3c93c3a5d76c21aec871a34523","schema_version":"1.0","event_id":"sha256:cf3ba84edc49382e179985f53518e4ba455c8b3c93c3a5d76c21aec871a34523"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:U463FE3ZGTXQLUBBNKITCGHWKV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stochastic Constraint Programming as Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Armagan Tarim, Roberto Rossi, Steven Prestwich","submitted_at":"2017-04-24T12:44:38Z","abstract_excerpt":"Stochastic Constraint Programming (SCP) is an extension of Constraint Programming (CP) used for modelling and solving problems involving constraints and uncertainty. SCP inherits excellent modelling abilities and filtering algorithms from CP, but so far it has not been applied to large problems. Reinforcement Learning (RL) extends Dynamic Programming to large stochastic problems, but is problem-specific and has no generic solvers. We propose a hybrid combining the scalability of RL with the modelling and constraint filtering methods of CP. We implement a prototype in a CP system and demonstrat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.07183","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:45:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fX8MiXN+Jg3usdX/QdGGXu8JaMQs15zhAnTjP+R1tU2v0uDOY2HXRGD7SLTYDvORSrwvY1f9PygRxmEcROYPBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T14:55:56.588988Z"},"content_sha256":"6a279ab68fb9fd83f3c5a67dc49fa2a4baad062d79d3e6e63b5e3c78cc519133","schema_version":"1.0","event_id":"sha256:6a279ab68fb9fd83f3c5a67dc49fa2a4baad062d79d3e6e63b5e3c78cc519133"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U463FE3ZGTXQLUBBNKITCGHWKV/bundle.json","state_url":"https://pith.science/pith/U463FE3ZGTXQLUBBNKITCGHWKV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U463FE3ZGTXQLUBBNKITCGHWKV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-23T14:55:56Z","links":{"resolver":"https://pith.science/pith/U463FE3ZGTXQLUBBNKITCGHWKV","bundle":"https://pith.science/pith/U463FE3ZGTXQLUBBNKITCGHWKV/bundle.json","state":"https://pith.science/pith/U463FE3ZGTXQLUBBNKITCGHWKV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U463FE3ZGTXQLUBBNKITCGHWKV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:U463FE3ZGTXQLUBBNKITCGHWKV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"703e5af52af2861d05dfd504cf0e61ccbff53089aaa1703caab289a69c8c82de","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-04-24T12:44:38Z","title_canon_sha256":"8c28c8b17dd7ac2a942b6e6acc2e100d926462a470fae4594df90faaa9f547dc"},"schema_version":"1.0","source":{"id":"1704.07183","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.07183","created_at":"2026-05-18T00:45:53Z"},{"alias_kind":"arxiv_version","alias_value":"1704.07183v1","created_at":"2026-05-18T00:45:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.07183","created_at":"2026-05-18T00:45:53Z"},{"alias_kind":"pith_short_12","alias_value":"U463FE3ZGTXQ","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"U463FE3ZGTXQLUBB","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"U463FE3Z","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:6a279ab68fb9fd83f3c5a67dc49fa2a4baad062d79d3e6e63b5e3c78cc519133","target":"graph","created_at":"2026-05-18T00:45:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Stochastic Constraint Programming (SCP) is an extension of Constraint Programming (CP) used for modelling and solving problems involving constraints and uncertainty. SCP inherits excellent modelling abilities and filtering algorithms from CP, but so far it has not been applied to large problems. Reinforcement Learning (RL) extends Dynamic Programming to large stochastic problems, but is problem-specific and has no generic solvers. We propose a hybrid combining the scalability of RL with the modelling and constraint filtering methods of CP. We implement a prototype in a CP system and demonstrat","authors_text":"Armagan Tarim, Roberto Rossi, Steven Prestwich","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-04-24T12:44:38Z","title":"Stochastic Constraint Programming as Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.07183","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:cf3ba84edc49382e179985f53518e4ba455c8b3c93c3a5d76c21aec871a34523","target":"record","created_at":"2026-05-18T00:45:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"703e5af52af2861d05dfd504cf0e61ccbff53089aaa1703caab289a69c8c82de","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-04-24T12:44:38Z","title_canon_sha256":"8c28c8b17dd7ac2a942b6e6acc2e100d926462a470fae4594df90faaa9f547dc"},"schema_version":"1.0","source":{"id":"1704.07183","kind":"arxiv","version":1}},"canonical_sha256":"a73db2937934ef05d0216a913118f65553ff755631987e8deee5d92e1ba088ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a73db2937934ef05d0216a913118f65553ff755631987e8deee5d92e1ba088ae","first_computed_at":"2026-05-18T00:45:53.294065Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:45:53.294065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yYDrw8EH89XYWfPem0V+RjWXPNvpS3vlOlsdgHwlJFARIOvxyzxdVEoV3cW/6UK0gDGYxbLO2lKO3GkeysDaBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:45:53.294688Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.07183","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cf3ba84edc49382e179985f53518e4ba455c8b3c93c3a5d76c21aec871a34523","sha256:6a279ab68fb9fd83f3c5a67dc49fa2a4baad062d79d3e6e63b5e3c78cc519133"],"state_sha256":"428681ff52ec004dfddfad1fa7c93cdc0700fd4ca10c36f4c5ee791492539e32"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SuzgRUI9CXNKOXzuLg7xJd4XukUoZhLw35yKfajGFrYLZlrxmLz6uEe1SGU4tV1Hy5taMfRpZfoeFyxYECxsBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T14:55:56.590925Z","bundle_sha256":"a1b9548b8b2e1201deea1cff4d9c2f469cd8e7d9b276332a12db67755fd9ffcb"}}