{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5ILGCRSWWPU5S7DR2FX24ZCQAR","short_pith_number":"pith:5ILGCRSW","schema_version":"1.0","canonical_sha256":"ea16614656b3e9d97c71d16fae64500460ca774d223016a69730557f3fb8c5cf","source":{"kind":"arxiv","id":"2303.02630","version":1},"attestation_state":"computed","paper":{"title":"D-HAL: Distributed Hierarchical Adversarial Learning for Multi-Agent Interaction in Autonomous Intersection Management","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.MA","authors_text":"Guanzhou Li, Jianping Wu, Yujing He","submitted_at":"2023-03-05T10:10:56Z","abstract_excerpt":"Autonomous Intersection Management (AIM) provides a signal-free intersection scheduling paradigm for Connected Autonomous Vehicles (CAVs). Distributed learning method has emerged as an attractive branch of AIM research. Compared with centralized AIM, distributed AIM can be deployed to CAVs at a lower cost, and compared with rule-based and optimization-based method, learning-based method can treat various complicated real-time intersection scenarios more flexibly. Deep reinforcement learning (DRL) is the mainstream approach in distributed learning to address AIM problems. However, the large-sca"},"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":"2303.02630","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2023-03-05T10:10:56Z","cross_cats_sorted":[],"title_canon_sha256":"b275ae983acaecfbbaa83a13bbae0964c2fc4f7d71d53e990d44454e265307da","abstract_canon_sha256":"63b4afce83dd42a032447bd04b236094f92449f2350cefcaea3e19df01988815"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:48:13.434815Z","signature_b64":"RSRz7Gvp0dPny2tWPzfLF87Cn8uVIc45c4Nso+9/pqt5Bc8fXLIwi6UgCyqeSJAj0f4K8nthQPcOCLvEaG7WDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea16614656b3e9d97c71d16fae64500460ca774d223016a69730557f3fb8c5cf","last_reissued_at":"2026-07-05T05:48:13.434376Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:48:13.434376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"D-HAL: Distributed Hierarchical Adversarial Learning for Multi-Agent Interaction in Autonomous Intersection Management","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.MA","authors_text":"Guanzhou Li, Jianping Wu, Yujing He","submitted_at":"2023-03-05T10:10:56Z","abstract_excerpt":"Autonomous Intersection Management (AIM) provides a signal-free intersection scheduling paradigm for Connected Autonomous Vehicles (CAVs). Distributed learning method has emerged as an attractive branch of AIM research. Compared with centralized AIM, distributed AIM can be deployed to CAVs at a lower cost, and compared with rule-based and optimization-based method, learning-based method can treat various complicated real-time intersection scenarios more flexibly. Deep reinforcement learning (DRL) is the mainstream approach in distributed learning to address AIM problems. However, the large-sca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.02630","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.02630/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2303.02630","created_at":"2026-07-05T05:48:13.434434+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.02630v1","created_at":"2026-07-05T05:48:13.434434+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.02630","created_at":"2026-07-05T05:48:13.434434+00:00"},{"alias_kind":"pith_short_12","alias_value":"5ILGCRSWWPU5","created_at":"2026-07-05T05:48:13.434434+00:00"},{"alias_kind":"pith_short_16","alias_value":"5ILGCRSWWPU5S7DR","created_at":"2026-07-05T05:48:13.434434+00:00"},{"alias_kind":"pith_short_8","alias_value":"5ILGCRSW","created_at":"2026-07-05T05:48:13.434434+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5ILGCRSWWPU5S7DR2FX24ZCQAR","json":"https://pith.science/pith/5ILGCRSWWPU5S7DR2FX24ZCQAR.json","graph_json":"https://pith.science/api/pith-number/5ILGCRSWWPU5S7DR2FX24ZCQAR/graph.json","events_json":"https://pith.science/api/pith-number/5ILGCRSWWPU5S7DR2FX24ZCQAR/events.json","paper":"https://pith.science/paper/5ILGCRSW"},"agent_actions":{"view_html":"https://pith.science/pith/5ILGCRSWWPU5S7DR2FX24ZCQAR","download_json":"https://pith.science/pith/5ILGCRSWWPU5S7DR2FX24ZCQAR.json","view_paper":"https://pith.science/paper/5ILGCRSW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.02630&json=true","fetch_graph":"https://pith.science/api/pith-number/5ILGCRSWWPU5S7DR2FX24ZCQAR/graph.json","fetch_events":"https://pith.science/api/pith-number/5ILGCRSWWPU5S7DR2FX24ZCQAR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5ILGCRSWWPU5S7DR2FX24ZCQAR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5ILGCRSWWPU5S7DR2FX24ZCQAR/action/storage_attestation","attest_author":"https://pith.science/pith/5ILGCRSWWPU5S7DR2FX24ZCQAR/action/author_attestation","sign_citation":"https://pith.science/pith/5ILGCRSWWPU5S7DR2FX24ZCQAR/action/citation_signature","submit_replication":"https://pith.science/pith/5ILGCRSWWPU5S7DR2FX24ZCQAR/action/replication_record"}},"created_at":"2026-07-05T05:48:13.434434+00:00","updated_at":"2026-07-05T05:48:13.434434+00:00"}