{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5SRPLZ7LQPSBZQZKMZTRXEGSQE","short_pith_number":"pith:5SRPLZ7L","schema_version":"1.0","canonical_sha256":"eca2f5e7eb83e41cc32a66671b90d2810c62cca904eeb116fff76920f9002ecc","source":{"kind":"arxiv","id":"1808.08441","version":1},"attestation_state":"computed","paper":{"title":"Inductive Learning of Answer Set Programs from Noisy Examples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Alessandra Russo, Krysia Broda, Mark Law","submitted_at":"2018-08-25T15:30:17Z","abstract_excerpt":"In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, allowing the learning of common-sense knowledge involving defaults and exceptions, which are essential aspects of human reasoning. In this paper, we present a noise-tolerant generalisation of the learning from answer sets framework. We evaluate our ILASP3 system, both on synthetic and on real datasets, represented in the new framework. In particular, we show that on many of the da"},"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":"1808.08441","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-08-25T15:30:17Z","cross_cats_sorted":[],"title_canon_sha256":"16e29b4680eede6fce574c8ac15af47ebcc8a22ede8081b353849063ab0b9ed4","abstract_canon_sha256":"07ba37f8482763e61419b243b10e8f746549ea4c4404f680d953984b3031314c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:16.482994Z","signature_b64":"JD0YJuityYcPt6Dq7B3Dx/bVmwvrN0jsStVtvwrVY+4RGGwsfytWqK4I2wvjqJmzLUyTKVAvg5cgOXfpgz9RCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eca2f5e7eb83e41cc32a66671b90d2810c62cca904eeb116fff76920f9002ecc","last_reissued_at":"2026-05-18T00:07:16.482394Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:16.482394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Inductive Learning of Answer Set Programs from Noisy Examples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Alessandra Russo, Krysia Broda, Mark Law","submitted_at":"2018-08-25T15:30:17Z","abstract_excerpt":"In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, allowing the learning of common-sense knowledge involving defaults and exceptions, which are essential aspects of human reasoning. In this paper, we present a noise-tolerant generalisation of the learning from answer sets framework. We evaluate our ILASP3 system, both on synthetic and on real datasets, represented in the new framework. In particular, we show that on many of the da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.08441","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":"1808.08441","created_at":"2026-05-18T00:07:16.482481+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.08441v1","created_at":"2026-05-18T00:07:16.482481+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.08441","created_at":"2026-05-18T00:07:16.482481+00:00"},{"alias_kind":"pith_short_12","alias_value":"5SRPLZ7LQPSB","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5SRPLZ7LQPSBZQZK","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5SRPLZ7L","created_at":"2026-05-18T12:32:08.215937+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.06838","citing_title":"Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5SRPLZ7LQPSBZQZKMZTRXEGSQE","json":"https://pith.science/pith/5SRPLZ7LQPSBZQZKMZTRXEGSQE.json","graph_json":"https://pith.science/api/pith-number/5SRPLZ7LQPSBZQZKMZTRXEGSQE/graph.json","events_json":"https://pith.science/api/pith-number/5SRPLZ7LQPSBZQZKMZTRXEGSQE/events.json","paper":"https://pith.science/paper/5SRPLZ7L"},"agent_actions":{"view_html":"https://pith.science/pith/5SRPLZ7LQPSBZQZKMZTRXEGSQE","download_json":"https://pith.science/pith/5SRPLZ7LQPSBZQZKMZTRXEGSQE.json","view_paper":"https://pith.science/paper/5SRPLZ7L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.08441&json=true","fetch_graph":"https://pith.science/api/pith-number/5SRPLZ7LQPSBZQZKMZTRXEGSQE/graph.json","fetch_events":"https://pith.science/api/pith-number/5SRPLZ7LQPSBZQZKMZTRXEGSQE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5SRPLZ7LQPSBZQZKMZTRXEGSQE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5SRPLZ7LQPSBZQZKMZTRXEGSQE/action/storage_attestation","attest_author":"https://pith.science/pith/5SRPLZ7LQPSBZQZKMZTRXEGSQE/action/author_attestation","sign_citation":"https://pith.science/pith/5SRPLZ7LQPSBZQZKMZTRXEGSQE/action/citation_signature","submit_replication":"https://pith.science/pith/5SRPLZ7LQPSBZQZKMZTRXEGSQE/action/replication_record"}},"created_at":"2026-05-18T00:07:16.482481+00:00","updated_at":"2026-05-18T00:07:16.482481+00:00"}