{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DPRAIM35GKUP4UTPMKEELJES6U","short_pith_number":"pith:DPRAIM35","schema_version":"1.0","canonical_sha256":"1be204337d32a8fe526f628845a492f52eb665db38b2c155b3d6eefbd96298d8","source":{"kind":"arxiv","id":"2308.15899","version":1},"attestation_state":"computed","paper":{"title":"Beyond Traditional Neural Networks: Toward adding Reasoning and Learning Capabilities through Computational Logic Techniques","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.LO","cs.MA"],"primary_cat":"cs.AI","authors_text":"Andrea Rafanelli (University of Pisa, Italy, Italy), University of L'Aquila","submitted_at":"2023-08-30T09:09:42Z","abstract_excerpt":"Deep Learning (DL) models have become popular for solving complex problems, but they have limitations such as the need for high-quality training data, lack of transparency, and robustness issues. Neuro-Symbolic AI has emerged as a promising approach combining the strengths of neural networks and symbolic reasoning. Symbolic knowledge injection (SKI) techniques are a popular method to incorporate symbolic knowledge into sub-symbolic systems. This work proposes solutions to improve the knowledge injection process and integrate elements of ML and logic into multi-agent systems (MAS)."},"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":"2308.15899","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-08-30T09:09:42Z","cross_cats_sorted":["cs.LG","cs.LO","cs.MA"],"title_canon_sha256":"52665faea227d2df89f2710140b916d7f0fb11f1f8fca04b5839520b82d2fb23","abstract_canon_sha256":"35fb43f752cd4724b90d9aea826f3797074ae7165ecda930a3aa9e0a6d262cca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:46:15.048075Z","signature_b64":"Ld6bOP6/FX0pAYVx4oBv8zEpH9+kz4u1vk13IjNmZDJLqduRRzIk1W3vHBhgz2WDEMwWLgNX4lgWLgusiq6CCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1be204337d32a8fe526f628845a492f52eb665db38b2c155b3d6eefbd96298d8","last_reissued_at":"2026-07-05T06:46:15.047525Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:46:15.047525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Traditional Neural Networks: Toward adding Reasoning and Learning Capabilities through Computational Logic Techniques","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.LO","cs.MA"],"primary_cat":"cs.AI","authors_text":"Andrea Rafanelli (University of Pisa, Italy, Italy), University of L'Aquila","submitted_at":"2023-08-30T09:09:42Z","abstract_excerpt":"Deep Learning (DL) models have become popular for solving complex problems, but they have limitations such as the need for high-quality training data, lack of transparency, and robustness issues. Neuro-Symbolic AI has emerged as a promising approach combining the strengths of neural networks and symbolic reasoning. Symbolic knowledge injection (SKI) techniques are a popular method to incorporate symbolic knowledge into sub-symbolic systems. This work proposes solutions to improve the knowledge injection process and integrate elements of ML and logic into multi-agent systems (MAS)."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.15899","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/2308.15899/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":"2308.15899","created_at":"2026-07-05T06:46:15.047634+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.15899v1","created_at":"2026-07-05T06:46:15.047634+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.15899","created_at":"2026-07-05T06:46:15.047634+00:00"},{"alias_kind":"pith_short_12","alias_value":"DPRAIM35GKUP","created_at":"2026-07-05T06:46:15.047634+00:00"},{"alias_kind":"pith_short_16","alias_value":"DPRAIM35GKUP4UTP","created_at":"2026-07-05T06:46:15.047634+00:00"},{"alias_kind":"pith_short_8","alias_value":"DPRAIM35","created_at":"2026-07-05T06:46:15.047634+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/DPRAIM35GKUP4UTPMKEELJES6U","json":"https://pith.science/pith/DPRAIM35GKUP4UTPMKEELJES6U.json","graph_json":"https://pith.science/api/pith-number/DPRAIM35GKUP4UTPMKEELJES6U/graph.json","events_json":"https://pith.science/api/pith-number/DPRAIM35GKUP4UTPMKEELJES6U/events.json","paper":"https://pith.science/paper/DPRAIM35"},"agent_actions":{"view_html":"https://pith.science/pith/DPRAIM35GKUP4UTPMKEELJES6U","download_json":"https://pith.science/pith/DPRAIM35GKUP4UTPMKEELJES6U.json","view_paper":"https://pith.science/paper/DPRAIM35","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.15899&json=true","fetch_graph":"https://pith.science/api/pith-number/DPRAIM35GKUP4UTPMKEELJES6U/graph.json","fetch_events":"https://pith.science/api/pith-number/DPRAIM35GKUP4UTPMKEELJES6U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DPRAIM35GKUP4UTPMKEELJES6U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DPRAIM35GKUP4UTPMKEELJES6U/action/storage_attestation","attest_author":"https://pith.science/pith/DPRAIM35GKUP4UTPMKEELJES6U/action/author_attestation","sign_citation":"https://pith.science/pith/DPRAIM35GKUP4UTPMKEELJES6U/action/citation_signature","submit_replication":"https://pith.science/pith/DPRAIM35GKUP4UTPMKEELJES6U/action/replication_record"}},"created_at":"2026-07-05T06:46:15.047634+00:00","updated_at":"2026-07-05T06:46:15.047634+00:00"}