{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:GJD2EKUVBPEQ23BWSLKO3XILV7","short_pith_number":"pith:GJD2EKUV","schema_version":"1.0","canonical_sha256":"3247a22a950bc90d6c3692d4eddd0bafd8379497e429b034774cbc37ae4957be","source":{"kind":"arxiv","id":"1905.10907","version":1},"attestation_state":"computed","paper":{"title":"Learning Policies from Human Data for Skat","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Christopher Solinas, Douglas Rebstock, Michael Buro","submitted_at":"2019-05-27T00:05:44Z","abstract_excerpt":"Decision-making in large imperfect information games is difficult. Thanks to recent success in Poker, Counterfactual Regret Minimization (CFR) methods have been at the forefront of research in these games. However, most of the success in large games comes with the use of a forward model and powerful state abstractions. In trick-taking card games like Bridge or Skat, large information sets and an inability to advance the simulation without fully determinizing the state make forward search problematic. Furthermore, state abstractions can be especially difficult to construct because the precise h"},"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":"1905.10907","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-05-27T00:05:44Z","cross_cats_sorted":[],"title_canon_sha256":"fc432d9ac699b7314fd5ac789c6db535a9524f4e1db03189669ba290e8588d99","abstract_canon_sha256":"4bde84f7d6cef5e7cc38c6b1e3361f2708f0827765bbfdbb916507535eb6fb3b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:03.449379Z","signature_b64":"JMO8nufSOggBK92VdAcohUQ0kr5YGMRZq6IzqEmm+uZDV0JDKcqw0RbT5lbVtUK///YNJ2r2gJY9awUtlonHBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3247a22a950bc90d6c3692d4eddd0bafd8379497e429b034774cbc37ae4957be","last_reissued_at":"2026-05-17T23:45:03.448759Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:03.448759Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Policies from Human Data for Skat","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Christopher Solinas, Douglas Rebstock, Michael Buro","submitted_at":"2019-05-27T00:05:44Z","abstract_excerpt":"Decision-making in large imperfect information games is difficult. Thanks to recent success in Poker, Counterfactual Regret Minimization (CFR) methods have been at the forefront of research in these games. However, most of the success in large games comes with the use of a forward model and powerful state abstractions. In trick-taking card games like Bridge or Skat, large information sets and an inability to advance the simulation without fully determinizing the state make forward search problematic. Furthermore, state abstractions can be especially difficult to construct because the precise h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10907","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":""},"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":"1905.10907","created_at":"2026-05-17T23:45:03.448847+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.10907v1","created_at":"2026-05-17T23:45:03.448847+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10907","created_at":"2026-05-17T23:45:03.448847+00:00"},{"alias_kind":"pith_short_12","alias_value":"GJD2EKUVBPEQ","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"GJD2EKUVBPEQ23BW","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"GJD2EKUV","created_at":"2026-05-18T12:33:18.533446+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.15294","citing_title":"Imperfect-Information Games on Quantum Computers: A Case Study in Skat","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GJD2EKUVBPEQ23BWSLKO3XILV7","json":"https://pith.science/pith/GJD2EKUVBPEQ23BWSLKO3XILV7.json","graph_json":"https://pith.science/api/pith-number/GJD2EKUVBPEQ23BWSLKO3XILV7/graph.json","events_json":"https://pith.science/api/pith-number/GJD2EKUVBPEQ23BWSLKO3XILV7/events.json","paper":"https://pith.science/paper/GJD2EKUV"},"agent_actions":{"view_html":"https://pith.science/pith/GJD2EKUVBPEQ23BWSLKO3XILV7","download_json":"https://pith.science/pith/GJD2EKUVBPEQ23BWSLKO3XILV7.json","view_paper":"https://pith.science/paper/GJD2EKUV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.10907&json=true","fetch_graph":"https://pith.science/api/pith-number/GJD2EKUVBPEQ23BWSLKO3XILV7/graph.json","fetch_events":"https://pith.science/api/pith-number/GJD2EKUVBPEQ23BWSLKO3XILV7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GJD2EKUVBPEQ23BWSLKO3XILV7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GJD2EKUVBPEQ23BWSLKO3XILV7/action/storage_attestation","attest_author":"https://pith.science/pith/GJD2EKUVBPEQ23BWSLKO3XILV7/action/author_attestation","sign_citation":"https://pith.science/pith/GJD2EKUVBPEQ23BWSLKO3XILV7/action/citation_signature","submit_replication":"https://pith.science/pith/GJD2EKUVBPEQ23BWSLKO3XILV7/action/replication_record"}},"created_at":"2026-05-17T23:45:03.448847+00:00","updated_at":"2026-05-17T23:45:03.448847+00:00"}