{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3RAORDLC3GEKFXL6U5YL2YB3U7","short_pith_number":"pith:3RAORDLC","schema_version":"1.0","canonical_sha256":"dc40e88d62d988a2dd7ea770bd603ba7e476881fc43be366643f60d212f462c3","source":{"kind":"arxiv","id":"2411.10558","version":1},"attestation_state":"computed","paper":{"title":"Multi-agent Path Finding for Timed Tasks using Evolutionary Games","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GT","cs.NE"],"primary_cat":"cs.MA","authors_text":"Anand Balakrishnan, Jyotirmoy V. Deshmukh, Sheryl Paul, Xin Qin","submitted_at":"2024-11-15T20:10:25Z","abstract_excerpt":"Autonomous multi-agent systems such as hospital robots and package delivery drones often operate in highly uncertain environments and are expected to achieve complex temporal task objectives while ensuring safety. While learning-based methods such as reinforcement learning are popular methods to train single and multi-agent autonomous systems under user-specified and state-based reward functions, applying these methods to satisfy trajectory-level task objectives is a challenging problem. Our first contribution is the use of weighted automata to specify trajectory-level objectives, such that, m"},"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":"2411.10558","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2024-11-15T20:10:25Z","cross_cats_sorted":["cs.GT","cs.NE"],"title_canon_sha256":"e9474f683ff63638cc751ee3b527798f72e1d50ed350295a07d219423ef8847c","abstract_canon_sha256":"b8b1ac5e26f342e7394d1b04009717077b83b36dc51b2d2df334cf80fa403a55"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:18.454693Z","signature_b64":"tHOYcZJcCh8lnV5SdZzpwFv58wKtd7HeR64c3m7af5Ues9Wb0SF5Q1kRFAuSJQafbz7a972tIJT/hWu34St/BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc40e88d62d988a2dd7ea770bd603ba7e476881fc43be366643f60d212f462c3","last_reissued_at":"2026-07-05T09:36:18.454265Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:18.454265Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-agent Path Finding for Timed Tasks using Evolutionary Games","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GT","cs.NE"],"primary_cat":"cs.MA","authors_text":"Anand Balakrishnan, Jyotirmoy V. Deshmukh, Sheryl Paul, Xin Qin","submitted_at":"2024-11-15T20:10:25Z","abstract_excerpt":"Autonomous multi-agent systems such as hospital robots and package delivery drones often operate in highly uncertain environments and are expected to achieve complex temporal task objectives while ensuring safety. While learning-based methods such as reinforcement learning are popular methods to train single and multi-agent autonomous systems under user-specified and state-based reward functions, applying these methods to satisfy trajectory-level task objectives is a challenging problem. Our first contribution is the use of weighted automata to specify trajectory-level objectives, such that, m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.10558","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/2411.10558/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":"2411.10558","created_at":"2026-07-05T09:36:18.454325+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.10558v1","created_at":"2026-07-05T09:36:18.454325+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.10558","created_at":"2026-07-05T09:36:18.454325+00:00"},{"alias_kind":"pith_short_12","alias_value":"3RAORDLC3GEK","created_at":"2026-07-05T09:36:18.454325+00:00"},{"alias_kind":"pith_short_16","alias_value":"3RAORDLC3GEKFXL6","created_at":"2026-07-05T09:36:18.454325+00:00"},{"alias_kind":"pith_short_8","alias_value":"3RAORDLC","created_at":"2026-07-05T09:36:18.454325+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/3RAORDLC3GEKFXL6U5YL2YB3U7","json":"https://pith.science/pith/3RAORDLC3GEKFXL6U5YL2YB3U7.json","graph_json":"https://pith.science/api/pith-number/3RAORDLC3GEKFXL6U5YL2YB3U7/graph.json","events_json":"https://pith.science/api/pith-number/3RAORDLC3GEKFXL6U5YL2YB3U7/events.json","paper":"https://pith.science/paper/3RAORDLC"},"agent_actions":{"view_html":"https://pith.science/pith/3RAORDLC3GEKFXL6U5YL2YB3U7","download_json":"https://pith.science/pith/3RAORDLC3GEKFXL6U5YL2YB3U7.json","view_paper":"https://pith.science/paper/3RAORDLC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.10558&json=true","fetch_graph":"https://pith.science/api/pith-number/3RAORDLC3GEKFXL6U5YL2YB3U7/graph.json","fetch_events":"https://pith.science/api/pith-number/3RAORDLC3GEKFXL6U5YL2YB3U7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3RAORDLC3GEKFXL6U5YL2YB3U7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3RAORDLC3GEKFXL6U5YL2YB3U7/action/storage_attestation","attest_author":"https://pith.science/pith/3RAORDLC3GEKFXL6U5YL2YB3U7/action/author_attestation","sign_citation":"https://pith.science/pith/3RAORDLC3GEKFXL6U5YL2YB3U7/action/citation_signature","submit_replication":"https://pith.science/pith/3RAORDLC3GEKFXL6U5YL2YB3U7/action/replication_record"}},"created_at":"2026-07-05T09:36:18.454325+00:00","updated_at":"2026-07-05T09:36:18.454325+00:00"}