{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:GMCYATZDTAV62T2MA6GOLKKNAL","short_pith_number":"pith:GMCYATZD","schema_version":"1.0","canonical_sha256":"3305804f23982bed4f4c078ce5a94d02e1102a7a8085fce547f30fbf27ab6b26","source":{"kind":"arxiv","id":"2006.08092","version":2},"attestation_state":"computed","paper":{"title":"An online evolving framework for advancing reinforcement-learning based automated vehicle control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO","cs.SY"],"primary_cat":"eess.SY","authors_text":"Dimitar P. Filev, Subramanya Nageshrao, Teawon Han, Umit Ozguner","submitted_at":"2020-06-15T02:27:23Z","abstract_excerpt":"In this paper, an online evolving framework is proposed to detect and revise a controller's imperfect decision-making in advance. The framework consists of three modules: the evolving Finite State Machine (e-FSM), action-reviser, and controller modules. The e-FSM module evolves a stochastic model (e.g., Discrete-Time Markov Chain) from scratch by determining new states and identifying transition probabilities repeatedly. With the latest stochastic model and given criteria, the action-reviser module checks validity of the controller's chosen action by predicting future states. Then, if the chos"},"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":"2006.08092","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2020-06-15T02:27:23Z","cross_cats_sorted":["cs.AI","cs.RO","cs.SY"],"title_canon_sha256":"3fe3824868c1744c65516427fc7ef6d1f8c602d1de752d4183ced0bbfa49d18c","abstract_canon_sha256":"dd90a54a3de9d0e513e388da74762238d34ce056b53d1efe27458d9608de4718"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:10:41.213511Z","signature_b64":"KgOGHjif2zi37a396QYOJstEyxc6SqVnTTpdhQL/+HR7nMA5OHOn4lsTzXi9ppcs81nCOhBD6iX3InK77mZ/Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3305804f23982bed4f4c078ce5a94d02e1102a7a8085fce547f30fbf27ab6b26","last_reissued_at":"2026-07-05T01:10:41.213112Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:10:41.213112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An online evolving framework for advancing reinforcement-learning based automated vehicle control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO","cs.SY"],"primary_cat":"eess.SY","authors_text":"Dimitar P. Filev, Subramanya Nageshrao, Teawon Han, Umit Ozguner","submitted_at":"2020-06-15T02:27:23Z","abstract_excerpt":"In this paper, an online evolving framework is proposed to detect and revise a controller's imperfect decision-making in advance. The framework consists of three modules: the evolving Finite State Machine (e-FSM), action-reviser, and controller modules. The e-FSM module evolves a stochastic model (e.g., Discrete-Time Markov Chain) from scratch by determining new states and identifying transition probabilities repeatedly. With the latest stochastic model and given criteria, the action-reviser module checks validity of the controller's chosen action by predicting future states. Then, if the chos"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.08092","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/2006.08092/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":"2006.08092","created_at":"2026-07-05T01:10:41.213172+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.08092v2","created_at":"2026-07-05T01:10:41.213172+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.08092","created_at":"2026-07-05T01:10:41.213172+00:00"},{"alias_kind":"pith_short_12","alias_value":"GMCYATZDTAV6","created_at":"2026-07-05T01:10:41.213172+00:00"},{"alias_kind":"pith_short_16","alias_value":"GMCYATZDTAV62T2M","created_at":"2026-07-05T01:10:41.213172+00:00"},{"alias_kind":"pith_short_8","alias_value":"GMCYATZD","created_at":"2026-07-05T01:10:41.213172+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/GMCYATZDTAV62T2MA6GOLKKNAL","json":"https://pith.science/pith/GMCYATZDTAV62T2MA6GOLKKNAL.json","graph_json":"https://pith.science/api/pith-number/GMCYATZDTAV62T2MA6GOLKKNAL/graph.json","events_json":"https://pith.science/api/pith-number/GMCYATZDTAV62T2MA6GOLKKNAL/events.json","paper":"https://pith.science/paper/GMCYATZD"},"agent_actions":{"view_html":"https://pith.science/pith/GMCYATZDTAV62T2MA6GOLKKNAL","download_json":"https://pith.science/pith/GMCYATZDTAV62T2MA6GOLKKNAL.json","view_paper":"https://pith.science/paper/GMCYATZD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.08092&json=true","fetch_graph":"https://pith.science/api/pith-number/GMCYATZDTAV62T2MA6GOLKKNAL/graph.json","fetch_events":"https://pith.science/api/pith-number/GMCYATZDTAV62T2MA6GOLKKNAL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GMCYATZDTAV62T2MA6GOLKKNAL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GMCYATZDTAV62T2MA6GOLKKNAL/action/storage_attestation","attest_author":"https://pith.science/pith/GMCYATZDTAV62T2MA6GOLKKNAL/action/author_attestation","sign_citation":"https://pith.science/pith/GMCYATZDTAV62T2MA6GOLKKNAL/action/citation_signature","submit_replication":"https://pith.science/pith/GMCYATZDTAV62T2MA6GOLKKNAL/action/replication_record"}},"created_at":"2026-07-05T01:10:41.213172+00:00","updated_at":"2026-07-05T01:10:41.213172+00:00"}