{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:XHGOEB2D5GTXUBW5CDQSKILXN3","short_pith_number":"pith:XHGOEB2D","schema_version":"1.0","canonical_sha256":"b9cce20743e9a77a06dd10e12521776ed613543e5b2c8b8361cb9edc04ccdf0b","source":{"kind":"arxiv","id":"1908.01362","version":2},"attestation_state":"computed","paper":{"title":"ASNets: Deep Learning for Generalised Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Felipe Trevizan, Lexing Xie, Sam Toyer, Sylvie Thi\\'ebaux","submitted_at":"2019-08-04T15:37:13Z","abstract_excerpt":"In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets). The ASNet is a neural network architecture that exploits the relational structure of (P)PDDL planning problems to learn a common set of weights that can be applied to any problem in a domain. By mimicking the actions chosen by a traditional, non-learning planner on a handful of small problems in a domain, ASNets are able to learn a generalised reactive policy that can quickly solve much larger instances from the domain. This work extends the AS"},"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":"1908.01362","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2019-08-04T15:37:13Z","cross_cats_sorted":[],"title_canon_sha256":"8486eb3a04d73f0919886a099dabb5905bf33c4bf52eb8f82338b6dc974eccaa","abstract_canon_sha256":"d738c23dc1a4b540b8d6bd53fc4e42389421d1486724a8f40fa588a4d7a4b1bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:00:12.912088Z","signature_b64":"s/GKdsjprTaQoXIvJCa0mm3NtYQL/skKD5vTjWpuVW3Tk4Bli8ts33HmXoBW7TBXjlyL3mg1QGy1zDUDkV2AAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9cce20743e9a77a06dd10e12521776ed613543e5b2c8b8361cb9edc04ccdf0b","last_reissued_at":"2026-07-05T01:00:12.911661Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:00:12.911661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ASNets: Deep Learning for Generalised Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Felipe Trevizan, Lexing Xie, Sam Toyer, Sylvie Thi\\'ebaux","submitted_at":"2019-08-04T15:37:13Z","abstract_excerpt":"In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets). The ASNet is a neural network architecture that exploits the relational structure of (P)PDDL planning problems to learn a common set of weights that can be applied to any problem in a domain. By mimicking the actions chosen by a traditional, non-learning planner on a handful of small problems in a domain, ASNets are able to learn a generalised reactive policy that can quickly solve much larger instances from the domain. This work extends the AS"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.01362","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/1908.01362/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":"1908.01362","created_at":"2026-07-05T01:00:12.911720+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.01362v2","created_at":"2026-07-05T01:00:12.911720+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.01362","created_at":"2026-07-05T01:00:12.911720+00:00"},{"alias_kind":"pith_short_12","alias_value":"XHGOEB2D5GTX","created_at":"2026-07-05T01:00:12.911720+00:00"},{"alias_kind":"pith_short_16","alias_value":"XHGOEB2D5GTXUBW5","created_at":"2026-07-05T01:00:12.911720+00:00"},{"alias_kind":"pith_short_8","alias_value":"XHGOEB2D","created_at":"2026-07-05T01:00:12.911720+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/XHGOEB2D5GTXUBW5CDQSKILXN3","json":"https://pith.science/pith/XHGOEB2D5GTXUBW5CDQSKILXN3.json","graph_json":"https://pith.science/api/pith-number/XHGOEB2D5GTXUBW5CDQSKILXN3/graph.json","events_json":"https://pith.science/api/pith-number/XHGOEB2D5GTXUBW5CDQSKILXN3/events.json","paper":"https://pith.science/paper/XHGOEB2D"},"agent_actions":{"view_html":"https://pith.science/pith/XHGOEB2D5GTXUBW5CDQSKILXN3","download_json":"https://pith.science/pith/XHGOEB2D5GTXUBW5CDQSKILXN3.json","view_paper":"https://pith.science/paper/XHGOEB2D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.01362&json=true","fetch_graph":"https://pith.science/api/pith-number/XHGOEB2D5GTXUBW5CDQSKILXN3/graph.json","fetch_events":"https://pith.science/api/pith-number/XHGOEB2D5GTXUBW5CDQSKILXN3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XHGOEB2D5GTXUBW5CDQSKILXN3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XHGOEB2D5GTXUBW5CDQSKILXN3/action/storage_attestation","attest_author":"https://pith.science/pith/XHGOEB2D5GTXUBW5CDQSKILXN3/action/author_attestation","sign_citation":"https://pith.science/pith/XHGOEB2D5GTXUBW5CDQSKILXN3/action/citation_signature","submit_replication":"https://pith.science/pith/XHGOEB2D5GTXUBW5CDQSKILXN3/action/replication_record"}},"created_at":"2026-07-05T01:00:12.911720+00:00","updated_at":"2026-07-05T01:00:12.911720+00:00"}