{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PHHZ3Y46FT7ZSTA5CJSN65CDKB","short_pith_number":"pith:PHHZ3Y46","schema_version":"1.0","canonical_sha256":"79cf9de39e2cff994c1d1264df7443504e35e514f9e6e2d694f5d1eb3a7dc2c0","source":{"kind":"arxiv","id":"2311.02099","version":4},"attestation_state":"computed","paper":{"title":"A Safe Preference Learning Approach for Personalization with Applications to Autonomous Vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.AI","authors_text":"Andrew Best, Jonathan DeCastro, Necmiye Ozay, Nikos Arechiga, Ruya Karagulle","submitted_at":"2023-10-30T21:52:37Z","abstract_excerpt":"This work introduces a preference learning method that ensures adherence to given specifications, with an application to autonomous vehicles. Our approach incorporates the priority ordering of Signal Temporal Logic (STL) formulas describing traffic rules into a learning framework. By leveraging Parametric Weighted Signal Temporal Logic (PWSTL), we formulate the problem of safety-guaranteed preference learning based on pairwise comparisons and propose an approach to solve this learning problem. Our approach finds a feasible valuation for the weights of the given PWSTL formula such that, with th"},"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":"2311.02099","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-10-30T21:52:37Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"278c546439030bd43ed4375d0af15b9641403a7ac466dc9743880f78d6f93174","abstract_canon_sha256":"9565c25c57ac55cc544266c08198204630ff89263bd9619a77b5585effb0f334"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:25:35.592217Z","signature_b64":"P5OlKSwbPoGuhYc/0LwT2jtiXy4EuiiCn1v9YWCexUCoSmBR5U1cAxwqUUdeIIYu0ZZ97tp9Fy0TFBpFm2FFCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79cf9de39e2cff994c1d1264df7443504e35e514f9e6e2d694f5d1eb3a7dc2c0","last_reissued_at":"2026-07-05T09:25:35.591718Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:25:35.591718Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Safe Preference Learning Approach for Personalization with Applications to Autonomous Vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.AI","authors_text":"Andrew Best, Jonathan DeCastro, Necmiye Ozay, Nikos Arechiga, Ruya Karagulle","submitted_at":"2023-10-30T21:52:37Z","abstract_excerpt":"This work introduces a preference learning method that ensures adherence to given specifications, with an application to autonomous vehicles. Our approach incorporates the priority ordering of Signal Temporal Logic (STL) formulas describing traffic rules into a learning framework. By leveraging Parametric Weighted Signal Temporal Logic (PWSTL), we formulate the problem of safety-guaranteed preference learning based on pairwise comparisons and propose an approach to solve this learning problem. Our approach finds a feasible valuation for the weights of the given PWSTL formula such that, with th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.02099","kind":"arxiv","version":4},"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/2311.02099/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":"2311.02099","created_at":"2026-07-05T09:25:35.591775+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.02099v4","created_at":"2026-07-05T09:25:35.591775+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.02099","created_at":"2026-07-05T09:25:35.591775+00:00"},{"alias_kind":"pith_short_12","alias_value":"PHHZ3Y46FT7Z","created_at":"2026-07-05T09:25:35.591775+00:00"},{"alias_kind":"pith_short_16","alias_value":"PHHZ3Y46FT7ZSTA5","created_at":"2026-07-05T09:25:35.591775+00:00"},{"alias_kind":"pith_short_8","alias_value":"PHHZ3Y46","created_at":"2026-07-05T09:25:35.591775+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/PHHZ3Y46FT7ZSTA5CJSN65CDKB","json":"https://pith.science/pith/PHHZ3Y46FT7ZSTA5CJSN65CDKB.json","graph_json":"https://pith.science/api/pith-number/PHHZ3Y46FT7ZSTA5CJSN65CDKB/graph.json","events_json":"https://pith.science/api/pith-number/PHHZ3Y46FT7ZSTA5CJSN65CDKB/events.json","paper":"https://pith.science/paper/PHHZ3Y46"},"agent_actions":{"view_html":"https://pith.science/pith/PHHZ3Y46FT7ZSTA5CJSN65CDKB","download_json":"https://pith.science/pith/PHHZ3Y46FT7ZSTA5CJSN65CDKB.json","view_paper":"https://pith.science/paper/PHHZ3Y46","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.02099&json=true","fetch_graph":"https://pith.science/api/pith-number/PHHZ3Y46FT7ZSTA5CJSN65CDKB/graph.json","fetch_events":"https://pith.science/api/pith-number/PHHZ3Y46FT7ZSTA5CJSN65CDKB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PHHZ3Y46FT7ZSTA5CJSN65CDKB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PHHZ3Y46FT7ZSTA5CJSN65CDKB/action/storage_attestation","attest_author":"https://pith.science/pith/PHHZ3Y46FT7ZSTA5CJSN65CDKB/action/author_attestation","sign_citation":"https://pith.science/pith/PHHZ3Y46FT7ZSTA5CJSN65CDKB/action/citation_signature","submit_replication":"https://pith.science/pith/PHHZ3Y46FT7ZSTA5CJSN65CDKB/action/replication_record"}},"created_at":"2026-07-05T09:25:35.591775+00:00","updated_at":"2026-07-05T09:25:35.591775+00:00"}