{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:S4S4DPGSCZHIY73MUJJ2KFP5IV","short_pith_number":"pith:S4S4DPGS","schema_version":"1.0","canonical_sha256":"9725c1bcd2164e8c7f6ca253a515fd45759f1837b425de6d35e3f0f84c09d868","source":{"kind":"arxiv","id":"2412.01656","version":1},"attestation_state":"computed","paper":{"title":"STLGame: Signal Temporal Logic Games in Adversarial Multi-Agent Systems","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.MA","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Cristian-Ioan Vasile, George Pappas, Hongrui Zheng, Rahul Mangharam, Shuo Yang","submitted_at":"2024-12-02T16:05:18Z","abstract_excerpt":"We study how to synthesize a robust and safe policy for autonomous systems under signal temporal logic (STL) tasks in adversarial settings against unknown dynamic agents. To ensure the worst-case STL satisfaction, we propose STLGame, a framework that models the multi-agent system as a two-player zero-sum game, where the ego agents try to maximize the STL satisfaction and other agents minimize it. STLGame aims to find a Nash equilibrium policy profile, which is the best case in terms of robustness against unseen opponent policies, by using the fictitious self-play (FSP) framework. FSP iterative"},"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":"2412.01656","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2024-12-02T16:05:18Z","cross_cats_sorted":["cs.MA","cs.SY","eess.SY"],"title_canon_sha256":"98e13fded88361e87dd62795a9981e6be36d81180cc5a1ed0cb280a1b7399a3f","abstract_canon_sha256":"95164041b693833820718041201cb931e814f50c468a94a4c24a61495c3bcc25"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:43:16.690337Z","signature_b64":"nBNRsiNm3FSYsDkhqBrw44AkQWYhaD+hHfJtBitmoMts8hSnKY447jyUph37hEjJfUAbeZ3H6DPZKtkbB95LAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9725c1bcd2164e8c7f6ca253a515fd45759f1837b425de6d35e3f0f84c09d868","last_reissued_at":"2026-07-05T09:43:16.689933Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:43:16.689933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"STLGame: Signal Temporal Logic Games in Adversarial Multi-Agent Systems","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.MA","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Cristian-Ioan Vasile, George Pappas, Hongrui Zheng, Rahul Mangharam, Shuo Yang","submitted_at":"2024-12-02T16:05:18Z","abstract_excerpt":"We study how to synthesize a robust and safe policy for autonomous systems under signal temporal logic (STL) tasks in adversarial settings against unknown dynamic agents. To ensure the worst-case STL satisfaction, we propose STLGame, a framework that models the multi-agent system as a two-player zero-sum game, where the ego agents try to maximize the STL satisfaction and other agents minimize it. STLGame aims to find a Nash equilibrium policy profile, which is the best case in terms of robustness against unseen opponent policies, by using the fictitious self-play (FSP) framework. FSP iterative"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.01656","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/2412.01656/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":"2412.01656","created_at":"2026-07-05T09:43:16.689983+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.01656v1","created_at":"2026-07-05T09:43:16.689983+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.01656","created_at":"2026-07-05T09:43:16.689983+00:00"},{"alias_kind":"pith_short_12","alias_value":"S4S4DPGSCZHI","created_at":"2026-07-05T09:43:16.689983+00:00"},{"alias_kind":"pith_short_16","alias_value":"S4S4DPGSCZHIY73M","created_at":"2026-07-05T09:43:16.689983+00:00"},{"alias_kind":"pith_short_8","alias_value":"S4S4DPGS","created_at":"2026-07-05T09:43:16.689983+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/S4S4DPGSCZHIY73MUJJ2KFP5IV","json":"https://pith.science/pith/S4S4DPGSCZHIY73MUJJ2KFP5IV.json","graph_json":"https://pith.science/api/pith-number/S4S4DPGSCZHIY73MUJJ2KFP5IV/graph.json","events_json":"https://pith.science/api/pith-number/S4S4DPGSCZHIY73MUJJ2KFP5IV/events.json","paper":"https://pith.science/paper/S4S4DPGS"},"agent_actions":{"view_html":"https://pith.science/pith/S4S4DPGSCZHIY73MUJJ2KFP5IV","download_json":"https://pith.science/pith/S4S4DPGSCZHIY73MUJJ2KFP5IV.json","view_paper":"https://pith.science/paper/S4S4DPGS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.01656&json=true","fetch_graph":"https://pith.science/api/pith-number/S4S4DPGSCZHIY73MUJJ2KFP5IV/graph.json","fetch_events":"https://pith.science/api/pith-number/S4S4DPGSCZHIY73MUJJ2KFP5IV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S4S4DPGSCZHIY73MUJJ2KFP5IV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S4S4DPGSCZHIY73MUJJ2KFP5IV/action/storage_attestation","attest_author":"https://pith.science/pith/S4S4DPGSCZHIY73MUJJ2KFP5IV/action/author_attestation","sign_citation":"https://pith.science/pith/S4S4DPGSCZHIY73MUJJ2KFP5IV/action/citation_signature","submit_replication":"https://pith.science/pith/S4S4DPGSCZHIY73MUJJ2KFP5IV/action/replication_record"}},"created_at":"2026-07-05T09:43:16.689983+00:00","updated_at":"2026-07-05T09:43:16.689983+00:00"}