{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:NQULUVLCGZWTGBAXECJ6OSPEYM","short_pith_number":"pith:NQULUVLC","canonical_record":{"source":{"id":"1905.10958","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-27T03:39:17Z","cross_cats_sorted":["cs.AI","cs.HC","stat.ML"],"title_canon_sha256":"6f40e5e6240ba4a3634a7e3164fd958eebf14ba2bf73aa15b37eec325fed5bc0","abstract_canon_sha256":"6a9faca365f8143ea09acc68d92af0df8e5e3d197443ba9dc6a15d213f2a7087"},"schema_version":"1.0"},"canonical_sha256":"6c28ba5562366d3304172093e749e4c338233f17981acb681ee98220b1859e1c","source":{"kind":"arxiv","id":"1905.10958","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.10958","created_at":"2026-07-05T00:20:43Z"},{"alias_kind":"arxiv_version","alias_value":"1905.10958v2","created_at":"2026-07-05T00:20:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10958","created_at":"2026-07-05T00:20:43Z"},{"alias_kind":"pith_short_12","alias_value":"NQULUVLCGZWT","created_at":"2026-07-05T00:20:43Z"},{"alias_kind":"pith_short_16","alias_value":"NQULUVLCGZWTGBAX","created_at":"2026-07-05T00:20:43Z"},{"alias_kind":"pith_short_8","alias_value":"NQULUVLC","created_at":"2026-07-05T00:20:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:NQULUVLCGZWTGBAXECJ6OSPEYM","target":"record","payload":{"canonical_record":{"source":{"id":"1905.10958","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-27T03:39:17Z","cross_cats_sorted":["cs.AI","cs.HC","stat.ML"],"title_canon_sha256":"6f40e5e6240ba4a3634a7e3164fd958eebf14ba2bf73aa15b37eec325fed5bc0","abstract_canon_sha256":"6a9faca365f8143ea09acc68d92af0df8e5e3d197443ba9dc6a15d213f2a7087"},"schema_version":"1.0"},"canonical_sha256":"6c28ba5562366d3304172093e749e4c338233f17981acb681ee98220b1859e1c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:20:43.308350Z","signature_b64":"SRjflLbiDwk6nwYQlyL/cBDiEOPrDT3rxi6I8u6NS8xxUGQYvQL8lNGIYzghtjIyXBltLV+Na49QKLQliP/HDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c28ba5562366d3304172093e749e4c338233f17981acb681ee98220b1859e1c","last_reissued_at":"2026-07-05T00:20:43.307876Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:20:43.307876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.10958","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:20:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sEGWNtFkn28TsTDScdDV8OKAJY4e3t5HvD+AJ4bCBv2+tE10i+aGwtZGXDlSbuY6RgKDXHtskW8S2GSnMNceCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:24:48.080418Z"},"content_sha256":"db1133be949f5939c8e28916f1bee02ea69f3ffc198862fcedaccb97691de316","schema_version":"1.0","event_id":"sha256:db1133be949f5939c8e28916f1bee02ea69f3ffc198862fcedaccb97691de316"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:NQULUVLCGZWTGBAXECJ6OSPEYM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Explainable Reinforcement Learning Through a Causal Lens","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Frank Vetere, Liz Sonenberg, Prashan Madumal, Tim Miller","submitted_at":"2019-05-27T03:39:17Z","abstract_excerpt":"Prevalent theories in cognitive science propose that humans understand and represent the knowledge of the world through causal relationships. In making sense of the world, we build causal models in our mind to encode cause-effect relations of events and use these to explain why new events happen. In this paper, we use causal models to derive causal explanations of behaviour of reinforcement learning agents. We present an approach that learns a structural causal model during reinforcement learning and encodes causal relationships between variables of interest. This model is then used to generat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10958","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1905.10958/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T00:20:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MQDT4Dl+BFKQN7tOiS7ayHYATc8Hc2/61Q/OwFTR5QBjVIu1neUD7fnH4nUtxflQlrTqgwDjKW8Q1qJN0R99CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:24:48.081376Z"},"content_sha256":"b34e412dc334ce8416c6dd0473fecadf25b023daac0d795e794ffca761ac6791","schema_version":"1.0","event_id":"sha256:b34e412dc334ce8416c6dd0473fecadf25b023daac0d795e794ffca761ac6791"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NQULUVLCGZWTGBAXECJ6OSPEYM/bundle.json","state_url":"https://pith.science/pith/NQULUVLCGZWTGBAXECJ6OSPEYM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NQULUVLCGZWTGBAXECJ6OSPEYM/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":"2026-08-08T20:24:48Z","links":{"resolver":"https://pith.science/pith/NQULUVLCGZWTGBAXECJ6OSPEYM","bundle":"https://pith.science/pith/NQULUVLCGZWTGBAXECJ6OSPEYM/bundle.json","state":"https://pith.science/pith/NQULUVLCGZWTGBAXECJ6OSPEYM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NQULUVLCGZWTGBAXECJ6OSPEYM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:NQULUVLCGZWTGBAXECJ6OSPEYM","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":"6a9faca365f8143ea09acc68d92af0df8e5e3d197443ba9dc6a15d213f2a7087","cross_cats_sorted":["cs.AI","cs.HC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-27T03:39:17Z","title_canon_sha256":"6f40e5e6240ba4a3634a7e3164fd958eebf14ba2bf73aa15b37eec325fed5bc0"},"schema_version":"1.0","source":{"id":"1905.10958","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.10958","created_at":"2026-07-05T00:20:43Z"},{"alias_kind":"arxiv_version","alias_value":"1905.10958v2","created_at":"2026-07-05T00:20:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10958","created_at":"2026-07-05T00:20:43Z"},{"alias_kind":"pith_short_12","alias_value":"NQULUVLCGZWT","created_at":"2026-07-05T00:20:43Z"},{"alias_kind":"pith_short_16","alias_value":"NQULUVLCGZWTGBAX","created_at":"2026-07-05T00:20:43Z"},{"alias_kind":"pith_short_8","alias_value":"NQULUVLC","created_at":"2026-07-05T00:20:43Z"}],"graph_snapshots":[{"event_id":"sha256:b34e412dc334ce8416c6dd0473fecadf25b023daac0d795e794ffca761ac6791","target":"graph","created_at":"2026-07-05T00:20:43Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1905.10958/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prevalent theories in cognitive science propose that humans understand and represent the knowledge of the world through causal relationships. In making sense of the world, we build causal models in our mind to encode cause-effect relations of events and use these to explain why new events happen. In this paper, we use causal models to derive causal explanations of behaviour of reinforcement learning agents. We present an approach that learns a structural causal model during reinforcement learning and encodes causal relationships between variables of interest. This model is then used to generat","authors_text":"Frank Vetere, Liz Sonenberg, Prashan Madumal, Tim Miller","cross_cats":["cs.AI","cs.HC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-27T03:39:17Z","title":"Explainable Reinforcement Learning Through a Causal Lens"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10958","kind":"arxiv","version":2},"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:db1133be949f5939c8e28916f1bee02ea69f3ffc198862fcedaccb97691de316","target":"record","created_at":"2026-07-05T00:20:43Z","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":"6a9faca365f8143ea09acc68d92af0df8e5e3d197443ba9dc6a15d213f2a7087","cross_cats_sorted":["cs.AI","cs.HC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-27T03:39:17Z","title_canon_sha256":"6f40e5e6240ba4a3634a7e3164fd958eebf14ba2bf73aa15b37eec325fed5bc0"},"schema_version":"1.0","source":{"id":"1905.10958","kind":"arxiv","version":2}},"canonical_sha256":"6c28ba5562366d3304172093e749e4c338233f17981acb681ee98220b1859e1c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c28ba5562366d3304172093e749e4c338233f17981acb681ee98220b1859e1c","first_computed_at":"2026-07-05T00:20:43.307876Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:20:43.307876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SRjflLbiDwk6nwYQlyL/cBDiEOPrDT3rxi6I8u6NS8xxUGQYvQL8lNGIYzghtjIyXBltLV+Na49QKLQliP/HDA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:20:43.308350Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.10958","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db1133be949f5939c8e28916f1bee02ea69f3ffc198862fcedaccb97691de316","sha256:b34e412dc334ce8416c6dd0473fecadf25b023daac0d795e794ffca761ac6791"],"state_sha256":"04064fdd645bbcc60b1e9cb8a0a6760413e7a4057562420b2484971f2172df16"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ejj0Iq2VC9dxS0g3z/Gs0FR6m43oFCTVnrypNydZVIYNo210I9Sleuk9JkdWj39X7MbK4rb1wd12LRSx1RSCDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T20:24:48.087099Z","bundle_sha256":"afd83174cd441e2384787e5f45882f7e5f6115b24314727eb2332d5374c0a4c2"}}