{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ECKA3ZKI37KLA3BZOAFYNV2DI7","short_pith_number":"pith:ECKA3ZKI","canonical_record":{"source":{"id":"1802.03642","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-10T19:12:03Z","cross_cats_sorted":[],"title_canon_sha256":"1a9cdda1bbd22ae5e57f540acbdf570c122dbec20c98e95480ae2486e9ae89e5","abstract_canon_sha256":"56a78747abf6de39a9c7e526c8d575c322c16d85a13d6bbb785113462fc37fc0"},"schema_version":"1.0"},"canonical_sha256":"20940de548dfd4b06c39700b86d74347fd28e670d6d0160e6e9c7a045ada02b4","source":{"kind":"arxiv","id":"1802.03642","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.03642","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"arxiv_version","alias_value":"1802.03642v1","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.03642","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"pith_short_12","alias_value":"ECKA3ZKI37KL","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"ECKA3ZKI37KLA3BZ","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"ECKA3ZKI","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ECKA3ZKI37KLA3BZOAFYNV2DI7","target":"record","payload":{"canonical_record":{"source":{"id":"1802.03642","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-10T19:12:03Z","cross_cats_sorted":[],"title_canon_sha256":"1a9cdda1bbd22ae5e57f540acbdf570c122dbec20c98e95480ae2486e9ae89e5","abstract_canon_sha256":"56a78747abf6de39a9c7e526c8d575c322c16d85a13d6bbb785113462fc37fc0"},"schema_version":"1.0"},"canonical_sha256":"20940de548dfd4b06c39700b86d74347fd28e670d6d0160e6e9c7a045ada02b4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:50.879862Z","signature_b64":"0NfmAsxNs7p2k0jm3G2OYd/924SAyE/9XxrwfbrgtshO6MQIBUM0xh49y7owulgZCS9H2xqYG+T/h3g/+JihDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"20940de548dfd4b06c39700b86d74347fd28e670d6d0160e6e9c7a045ada02b4","last_reissued_at":"2026-05-18T00:23:50.879181Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:50.879181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.03642","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":"2026-05-18T00:23:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lnQqKvDw9+humaKJKy1zwlGaP/mS7TLTCx7GB9VAiCG8IhhnQKUNZFTHf9rDlvt1XC/E45tNv4Ix2on0BJTaAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:34:54.237607Z"},"content_sha256":"fab375a00f0feef40039e5ce16eee515ff9c271192f10331dc84e56eef98761c","schema_version":"1.0","event_id":"sha256:fab375a00f0feef40039e5ce16eee515ff9c271192f10331dc84e56eef98761c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ECKA3ZKI37KLA3BZOAFYNV2DI7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Planning with Expected Finite Horizon","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Krishnendu Chatterjee, Laurent Doyen","submitted_at":"2018-02-10T19:12:03Z","abstract_excerpt":"Graph planning gives rise to fundamental algorithmic questions such as shortest path, traveling salesman problem, etc. A classical problem in discrete planning is to consider a weighted graph and construct a path that maximizes the sum of weights for a given time horizon $T$. However, in many scenarios, the time horizon is not fixed, but the stopping time is chosen according to some distribution such that the expected stopping time is $T$. If the stopping time distribution is not known, then to ensure robustness, the distribution is chosen by an adversary, to represent the worst-case scenario."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.03642","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":"2026-05-18T00:23:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v9NJ/Qmw52jH2KRCIJMdUv4WOWd9XxxUAJDUhHoza9MPd9Pr0qPn5vYo8uF6bt54vvSycQptZv1U6ZI4Jgk3DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:34:54.238124Z"},"content_sha256":"a18a323835ba6e6a7b6ccf7fc6ab41cea94cc179440d4f93cdbd34859a48d40e","schema_version":"1.0","event_id":"sha256:a18a323835ba6e6a7b6ccf7fc6ab41cea94cc179440d4f93cdbd34859a48d40e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ECKA3ZKI37KLA3BZOAFYNV2DI7/bundle.json","state_url":"https://pith.science/pith/ECKA3ZKI37KLA3BZOAFYNV2DI7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ECKA3ZKI37KLA3BZOAFYNV2DI7/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":"2026-05-26T02:34:54Z","links":{"resolver":"https://pith.science/pith/ECKA3ZKI37KLA3BZOAFYNV2DI7","bundle":"https://pith.science/pith/ECKA3ZKI37KLA3BZOAFYNV2DI7/bundle.json","state":"https://pith.science/pith/ECKA3ZKI37KLA3BZOAFYNV2DI7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ECKA3ZKI37KLA3BZOAFYNV2DI7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ECKA3ZKI37KLA3BZOAFYNV2DI7","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":"56a78747abf6de39a9c7e526c8d575c322c16d85a13d6bbb785113462fc37fc0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-10T19:12:03Z","title_canon_sha256":"1a9cdda1bbd22ae5e57f540acbdf570c122dbec20c98e95480ae2486e9ae89e5"},"schema_version":"1.0","source":{"id":"1802.03642","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.03642","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"arxiv_version","alias_value":"1802.03642v1","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.03642","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"pith_short_12","alias_value":"ECKA3ZKI37KL","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"ECKA3ZKI37KLA3BZ","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"ECKA3ZKI","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:a18a323835ba6e6a7b6ccf7fc6ab41cea94cc179440d4f93cdbd34859a48d40e","target":"graph","created_at":"2026-05-18T00:23:50Z","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":"Graph planning gives rise to fundamental algorithmic questions such as shortest path, traveling salesman problem, etc. A classical problem in discrete planning is to consider a weighted graph and construct a path that maximizes the sum of weights for a given time horizon $T$. However, in many scenarios, the time horizon is not fixed, but the stopping time is chosen according to some distribution such that the expected stopping time is $T$. If the stopping time distribution is not known, then to ensure robustness, the distribution is chosen by an adversary, to represent the worst-case scenario.","authors_text":"Krishnendu Chatterjee, Laurent Doyen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-10T19:12:03Z","title":"Graph Planning with Expected Finite Horizon"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.03642","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:fab375a00f0feef40039e5ce16eee515ff9c271192f10331dc84e56eef98761c","target":"record","created_at":"2026-05-18T00:23:50Z","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":"56a78747abf6de39a9c7e526c8d575c322c16d85a13d6bbb785113462fc37fc0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-10T19:12:03Z","title_canon_sha256":"1a9cdda1bbd22ae5e57f540acbdf570c122dbec20c98e95480ae2486e9ae89e5"},"schema_version":"1.0","source":{"id":"1802.03642","kind":"arxiv","version":1}},"canonical_sha256":"20940de548dfd4b06c39700b86d74347fd28e670d6d0160e6e9c7a045ada02b4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"20940de548dfd4b06c39700b86d74347fd28e670d6d0160e6e9c7a045ada02b4","first_computed_at":"2026-05-18T00:23:50.879181Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:23:50.879181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0NfmAsxNs7p2k0jm3G2OYd/924SAyE/9XxrwfbrgtshO6MQIBUM0xh49y7owulgZCS9H2xqYG+T/h3g/+JihDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:23:50.879862Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.03642","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fab375a00f0feef40039e5ce16eee515ff9c271192f10331dc84e56eef98761c","sha256:a18a323835ba6e6a7b6ccf7fc6ab41cea94cc179440d4f93cdbd34859a48d40e"],"state_sha256":"8ab6ddea8c8dd40e3ef1e190784536bd62c007d023e65f0a7f82bb58d715ceec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2yCFXHKbwTTiYcp26ol1FOUop8g5NHs19L2VdJ9YPU+9UpgXhzQO3UxMHaYUpVT4emu+ln+XS2SdJbERZpSGCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T02:34:54.240738Z","bundle_sha256":"2ddbb4fba8e8d1dffb20b519f9eede679120734c6194812d34576128c5c72071"}}