{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:VI7V2NNMANI4NAHO7EVRT7AC2K","short_pith_number":"pith:VI7V2NNM","canonical_record":{"source":{"id":"1405.5498","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-05-21T18:01:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bc9370c764d9909f835a5423e844ef54d7b1e50997dcee7b57fc5abdcabaa465","abstract_canon_sha256":"c85287d5b38b12d7e67afb8012a1a77114cdaf2460f6e8fcbfd167c4d6943d2e"},"schema_version":"1.0"},"canonical_sha256":"aa3f5d35ac0351c680eef92b19fc02d292fd4eb838c65b7d6617ba700a0eef53","source":{"kind":"arxiv","id":"1405.5498","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.5498","created_at":"2026-05-18T02:51:23Z"},{"alias_kind":"arxiv_version","alias_value":"1405.5498v1","created_at":"2026-05-18T02:51:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.5498","created_at":"2026-05-18T02:51:23Z"},{"alias_kind":"pith_short_12","alias_value":"VI7V2NNMANI4","created_at":"2026-05-18T12:28:54Z"},{"alias_kind":"pith_short_16","alias_value":"VI7V2NNMANI4NAHO","created_at":"2026-05-18T12:28:54Z"},{"alias_kind":"pith_short_8","alias_value":"VI7V2NNM","created_at":"2026-05-18T12:28:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:VI7V2NNMANI4NAHO7EVRT7AC2K","target":"record","payload":{"canonical_record":{"source":{"id":"1405.5498","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-05-21T18:01:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bc9370c764d9909f835a5423e844ef54d7b1e50997dcee7b57fc5abdcabaa465","abstract_canon_sha256":"c85287d5b38b12d7e67afb8012a1a77114cdaf2460f6e8fcbfd167c4d6943d2e"},"schema_version":"1.0"},"canonical_sha256":"aa3f5d35ac0351c680eef92b19fc02d292fd4eb838c65b7d6617ba700a0eef53","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:51:23.876778Z","signature_b64":"ReoJ3g4qSvF3o5lEoh+W1kkgqsgsyDHk5zF6h2prY9YSkxcOrRTiwi0Tu0SJZatv+TdpTA29Wz3NIzLZy/ARBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa3f5d35ac0351c680eef92b19fc02d292fd4eb838c65b7d6617ba700a0eef53","last_reissued_at":"2026-05-18T02:51:23.876225Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:51:23.876225Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1405.5498","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":"2026-05-18T02:51:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q2GPlDjxgsirbGAAjlK+zqMj7xQVdnAONHSAS0ia2fkoLMDwXuBjtYTs67ktfHx7ya84R3pOTFIKaCqSsS5EBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-15T21:52:15.606993Z"},"content_sha256":"e401f81f84b4eae5cd92ad1ff66448fa03700070053afc5c80b7d6eb1c365719","schema_version":"1.0","event_id":"sha256:e401f81f84b4eae5cd92ad1ff66448fa03700070053afc5c80b7d6eb1c365719"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:VI7V2NNMANI4NAHO7EVRT7AC2K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Comparison of Monte Carlo Tree Search and Mathematical Optimization for Large Scale Dynamic Resource Allocation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"math.OC","authors_text":"Dimitris Bertsimas, J. Daniel Griffith, Mykel J. Kochenderfer, Robert Moss, Velibor V. Mi\\v{s}i\\'c, Vishal Gupta","submitted_at":"2014-05-21T18:01:20Z","abstract_excerpt":"Dynamic resource allocation (DRA) problems are an important class of dynamic stochastic optimization problems that arise in a variety of important real-world applications. DRA problems are notoriously difficult to solve to optimality since they frequently combine stochastic elements with intractably large state and action spaces. Although the artificial intelligence and operations research communities have independently proposed two successful frameworks for solving dynamic stochastic optimization problems---Monte Carlo tree search (MCTS) and mathematical optimization (MO), respectively---the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.5498","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":"2026-05-18T02:51:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mnfmwb/RqNRXPtDfoD286lkem1fjOwKWdxkJc1rA4i53i+8JcPjb2MTrOcrbkqv0Tl+AHH2n2RNFpubqIp8QAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-15T21:52:15.607356Z"},"content_sha256":"f3516442d0ec5ec364b0fbe4cfe9a0c17f271acb53a05cf9f9598db6e0ff0eb9","schema_version":"1.0","event_id":"sha256:f3516442d0ec5ec364b0fbe4cfe9a0c17f271acb53a05cf9f9598db6e0ff0eb9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VI7V2NNMANI4NAHO7EVRT7AC2K/bundle.json","state_url":"https://pith.science/pith/VI7V2NNMANI4NAHO7EVRT7AC2K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VI7V2NNMANI4NAHO7EVRT7AC2K/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":"2026-07-15T21:52:15Z","links":{"resolver":"https://pith.science/pith/VI7V2NNMANI4NAHO7EVRT7AC2K","bundle":"https://pith.science/pith/VI7V2NNMANI4NAHO7EVRT7AC2K/bundle.json","state":"https://pith.science/pith/VI7V2NNMANI4NAHO7EVRT7AC2K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VI7V2NNMANI4NAHO7EVRT7AC2K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:VI7V2NNMANI4NAHO7EVRT7AC2K","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":"c85287d5b38b12d7e67afb8012a1a77114cdaf2460f6e8fcbfd167c4d6943d2e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-05-21T18:01:20Z","title_canon_sha256":"bc9370c764d9909f835a5423e844ef54d7b1e50997dcee7b57fc5abdcabaa465"},"schema_version":"1.0","source":{"id":"1405.5498","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.5498","created_at":"2026-05-18T02:51:23Z"},{"alias_kind":"arxiv_version","alias_value":"1405.5498v1","created_at":"2026-05-18T02:51:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.5498","created_at":"2026-05-18T02:51:23Z"},{"alias_kind":"pith_short_12","alias_value":"VI7V2NNMANI4","created_at":"2026-05-18T12:28:54Z"},{"alias_kind":"pith_short_16","alias_value":"VI7V2NNMANI4NAHO","created_at":"2026-05-18T12:28:54Z"},{"alias_kind":"pith_short_8","alias_value":"VI7V2NNM","created_at":"2026-05-18T12:28:54Z"}],"graph_snapshots":[{"event_id":"sha256:f3516442d0ec5ec364b0fbe4cfe9a0c17f271acb53a05cf9f9598db6e0ff0eb9","target":"graph","created_at":"2026-05-18T02:51:23Z","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":"Dynamic resource allocation (DRA) problems are an important class of dynamic stochastic optimization problems that arise in a variety of important real-world applications. DRA problems are notoriously difficult to solve to optimality since they frequently combine stochastic elements with intractably large state and action spaces. Although the artificial intelligence and operations research communities have independently proposed two successful frameworks for solving dynamic stochastic optimization problems---Monte Carlo tree search (MCTS) and mathematical optimization (MO), respectively---the ","authors_text":"Dimitris Bertsimas, J. Daniel Griffith, Mykel J. Kochenderfer, Robert Moss, Velibor V. Mi\\v{s}i\\'c, Vishal Gupta","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-05-21T18:01:20Z","title":"A Comparison of Monte Carlo Tree Search and Mathematical Optimization for Large Scale Dynamic Resource Allocation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.5498","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:e401f81f84b4eae5cd92ad1ff66448fa03700070053afc5c80b7d6eb1c365719","target":"record","created_at":"2026-05-18T02:51:23Z","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":"c85287d5b38b12d7e67afb8012a1a77114cdaf2460f6e8fcbfd167c4d6943d2e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-05-21T18:01:20Z","title_canon_sha256":"bc9370c764d9909f835a5423e844ef54d7b1e50997dcee7b57fc5abdcabaa465"},"schema_version":"1.0","source":{"id":"1405.5498","kind":"arxiv","version":1}},"canonical_sha256":"aa3f5d35ac0351c680eef92b19fc02d292fd4eb838c65b7d6617ba700a0eef53","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aa3f5d35ac0351c680eef92b19fc02d292fd4eb838c65b7d6617ba700a0eef53","first_computed_at":"2026-05-18T02:51:23.876225Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:51:23.876225Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ReoJ3g4qSvF3o5lEoh+W1kkgqsgsyDHk5zF6h2prY9YSkxcOrRTiwi0Tu0SJZatv+TdpTA29Wz3NIzLZy/ARBA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:51:23.876778Z","signed_message":"canonical_sha256_bytes"},"source_id":"1405.5498","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e401f81f84b4eae5cd92ad1ff66448fa03700070053afc5c80b7d6eb1c365719","sha256:f3516442d0ec5ec364b0fbe4cfe9a0c17f271acb53a05cf9f9598db6e0ff0eb9"],"state_sha256":"5b7f2773315084136d2bff2fa48fe2a47e679d284aeb5c8319c4baaf2832578e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DjZZkJ3zN2p6OvRl3PP5/z1DyAVvMNDtUaFicD+YJjuw1uhfEI4FvyTzDllu/B3MIYZ7M0f/Xb7yEauRugW4AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-15T21:52:15.609472Z","bundle_sha256":"b5a708a952c7071228cb80018997995972f7ac94a6a9bc659b42417ca61ad23a"}}