{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:RVW3DBPILRG2AIXIVHCLWZC6B7","short_pith_number":"pith:RVW3DBPI","schema_version":"1.0","canonical_sha256":"8d6db185e85c4da022e8a9c4bb645e0fff93708eb535e829127b645f75f2be84","source":{"kind":"arxiv","id":"1910.13645","version":3},"attestation_state":"computed","paper":{"title":"Automatic Testing With Reusable Adversarial Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Andrew Best, Jyotirmoy Deshmukh, Nikos Ar\\'echiga, Xin Qin","submitted_at":"2019-10-30T03:18:10Z","abstract_excerpt":"Autonomous systems such as self-driving cars and general-purpose robots are safety-critical systems that operate in highly uncertain and dynamic environments. We propose an interactive multi-agent framework where the system-under-design is modeled as an ego agent and its environment is modeled by a number of adversarial (ado) agents. For example, a self-driving car is an ego agent whose behavior is influenced by ado agents such as pedestrians, bicyclists, traffic lights, road geometry etc. Given a logical specification of the correct behavior of the ego agent, and a set of constraints that enc"},"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":"1910.13645","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-30T03:18:10Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"title_canon_sha256":"1be190597c2fce9462bbc474199f0891920da5f4e80b16032190274dfd92a2a4","abstract_canon_sha256":"58711214aa29d70168bdf5af6bb1b47fd5754b153b0285db95a10147847f9c4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:55:05.189420Z","signature_b64":"zzJJlGeQI/ibUExawSA+08XIJqxjfbFGKlwP7FHnrO50G5c0zYHfn8BGpLJpuCjva3TDzGELXLr+H+Le3RoJCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d6db185e85c4da022e8a9c4bb645e0fff93708eb535e829127b645f75f2be84","last_reissued_at":"2026-07-05T02:55:05.189000Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:55:05.189000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Testing With Reusable Adversarial Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Andrew Best, Jyotirmoy Deshmukh, Nikos Ar\\'echiga, Xin Qin","submitted_at":"2019-10-30T03:18:10Z","abstract_excerpt":"Autonomous systems such as self-driving cars and general-purpose robots are safety-critical systems that operate in highly uncertain and dynamic environments. We propose an interactive multi-agent framework where the system-under-design is modeled as an ego agent and its environment is modeled by a number of adversarial (ado) agents. For example, a self-driving car is an ego agent whose behavior is influenced by ado agents such as pedestrians, bicyclists, traffic lights, road geometry etc. Given a logical specification of the correct behavior of the ego agent, and a set of constraints that enc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.13645","kind":"arxiv","version":3},"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/1910.13645/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":"1910.13645","created_at":"2026-07-05T02:55:05.189061+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.13645v3","created_at":"2026-07-05T02:55:05.189061+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.13645","created_at":"2026-07-05T02:55:05.189061+00:00"},{"alias_kind":"pith_short_12","alias_value":"RVW3DBPILRG2","created_at":"2026-07-05T02:55:05.189061+00:00"},{"alias_kind":"pith_short_16","alias_value":"RVW3DBPILRG2AIXI","created_at":"2026-07-05T02:55:05.189061+00:00"},{"alias_kind":"pith_short_8","alias_value":"RVW3DBPI","created_at":"2026-07-05T02:55:05.189061+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/RVW3DBPILRG2AIXIVHCLWZC6B7","json":"https://pith.science/pith/RVW3DBPILRG2AIXIVHCLWZC6B7.json","graph_json":"https://pith.science/api/pith-number/RVW3DBPILRG2AIXIVHCLWZC6B7/graph.json","events_json":"https://pith.science/api/pith-number/RVW3DBPILRG2AIXIVHCLWZC6B7/events.json","paper":"https://pith.science/paper/RVW3DBPI"},"agent_actions":{"view_html":"https://pith.science/pith/RVW3DBPILRG2AIXIVHCLWZC6B7","download_json":"https://pith.science/pith/RVW3DBPILRG2AIXIVHCLWZC6B7.json","view_paper":"https://pith.science/paper/RVW3DBPI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.13645&json=true","fetch_graph":"https://pith.science/api/pith-number/RVW3DBPILRG2AIXIVHCLWZC6B7/graph.json","fetch_events":"https://pith.science/api/pith-number/RVW3DBPILRG2AIXIVHCLWZC6B7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RVW3DBPILRG2AIXIVHCLWZC6B7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RVW3DBPILRG2AIXIVHCLWZC6B7/action/storage_attestation","attest_author":"https://pith.science/pith/RVW3DBPILRG2AIXIVHCLWZC6B7/action/author_attestation","sign_citation":"https://pith.science/pith/RVW3DBPILRG2AIXIVHCLWZC6B7/action/citation_signature","submit_replication":"https://pith.science/pith/RVW3DBPILRG2AIXIVHCLWZC6B7/action/replication_record"}},"created_at":"2026-07-05T02:55:05.189061+00:00","updated_at":"2026-07-05T02:55:05.189061+00:00"}