{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:EF7EMRQ2MVRX6E5BRUXN3DLJRZ","short_pith_number":"pith:EF7EMRQ2","schema_version":"1.0","canonical_sha256":"217e46461a65637f13a18d2edd8d698e6d01578728669cb5c78c7816e9ff28a1","source":{"kind":"arxiv","id":"1405.0720","version":1},"attestation_state":"computed","paper":{"title":"Probabilistic Inductive Logic Programming Based on Answer Set Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Alessandra Mileo, Matthias Nickles","submitted_at":"2014-05-04T17:18:49Z","abstract_excerpt":"We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in terms of soft and hard constraints which determine a probability distribution over answer sets. In contrast to related approaches, we approach inference by optionally utilizing so-called streamlining XOR constraints, in order to reduce the number of comp"},"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":"1405.0720","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-05-04T17:18:49Z","cross_cats_sorted":[],"title_canon_sha256":"746464be908d151893aac3c11ea9091f3b418273dc98a627a12c1a7515b4e7be","abstract_canon_sha256":"492a6ca94a2cae8d2a83e689d943dd0a6a474b3424c294dde16802e8171ae66b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:52:40.830593Z","signature_b64":"SDapmjpcBjYdhL8Rr8XABEnr4M3HAcV1Il1cxO6EjRuj+DuN1dDcFnMOeYTHGIqfnj71ukmmtoFlkfcN75PeAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"217e46461a65637f13a18d2edd8d698e6d01578728669cb5c78c7816e9ff28a1","last_reissued_at":"2026-05-18T02:52:40.829911Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:52:40.829911Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic Inductive Logic Programming Based on Answer Set Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Alessandra Mileo, Matthias Nickles","submitted_at":"2014-05-04T17:18:49Z","abstract_excerpt":"We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in terms of soft and hard constraints which determine a probability distribution over answer sets. In contrast to related approaches, we approach inference by optionally utilizing so-called streamlining XOR constraints, in order to reduce the number of comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.0720","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":"1405.0720","created_at":"2026-05-18T02:52:40.830012+00:00"},{"alias_kind":"arxiv_version","alias_value":"1405.0720v1","created_at":"2026-05-18T02:52:40.830012+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.0720","created_at":"2026-05-18T02:52:40.830012+00:00"},{"alias_kind":"pith_short_12","alias_value":"EF7EMRQ2MVRX","created_at":"2026-05-18T12:28:25.294606+00:00"},{"alias_kind":"pith_short_16","alias_value":"EF7EMRQ2MVRX6E5B","created_at":"2026-05-18T12:28:25.294606+00:00"},{"alias_kind":"pith_short_8","alias_value":"EF7EMRQ2","created_at":"2026-05-18T12:28:25.294606+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/EF7EMRQ2MVRX6E5BRUXN3DLJRZ","json":"https://pith.science/pith/EF7EMRQ2MVRX6E5BRUXN3DLJRZ.json","graph_json":"https://pith.science/api/pith-number/EF7EMRQ2MVRX6E5BRUXN3DLJRZ/graph.json","events_json":"https://pith.science/api/pith-number/EF7EMRQ2MVRX6E5BRUXN3DLJRZ/events.json","paper":"https://pith.science/paper/EF7EMRQ2"},"agent_actions":{"view_html":"https://pith.science/pith/EF7EMRQ2MVRX6E5BRUXN3DLJRZ","download_json":"https://pith.science/pith/EF7EMRQ2MVRX6E5BRUXN3DLJRZ.json","view_paper":"https://pith.science/paper/EF7EMRQ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1405.0720&json=true","fetch_graph":"https://pith.science/api/pith-number/EF7EMRQ2MVRX6E5BRUXN3DLJRZ/graph.json","fetch_events":"https://pith.science/api/pith-number/EF7EMRQ2MVRX6E5BRUXN3DLJRZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EF7EMRQ2MVRX6E5BRUXN3DLJRZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EF7EMRQ2MVRX6E5BRUXN3DLJRZ/action/storage_attestation","attest_author":"https://pith.science/pith/EF7EMRQ2MVRX6E5BRUXN3DLJRZ/action/author_attestation","sign_citation":"https://pith.science/pith/EF7EMRQ2MVRX6E5BRUXN3DLJRZ/action/citation_signature","submit_replication":"https://pith.science/pith/EF7EMRQ2MVRX6E5BRUXN3DLJRZ/action/replication_record"}},"created_at":"2026-05-18T02:52:40.830012+00:00","updated_at":"2026-05-18T02:52:40.830012+00:00"}