{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:YMME7PUJKAAIF26FTTR6JYTPBH","short_pith_number":"pith:YMME7PUJ","schema_version":"1.0","canonical_sha256":"c3184fbe89500082ebc59ce3e4e26f09cf50ab582bc8f695748ed20360d96c10","source":{"kind":"arxiv","id":"2107.10429","version":1},"attestation_state":"computed","paper":{"title":"Shedding some light on Light Up with Artificial Intelligence","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.AI","authors_text":"James Browning, Libo Sun, Roberto Perera","submitted_at":"2021-07-22T03:03:57Z","abstract_excerpt":"The Light-Up puzzle, also known as the AKARI puzzle, has never been solved using modern artificial intelligence (AI) methods. Currently, the most widely used computational technique to autonomously develop solutions involve evolution theory algorithms. This project is an effort to apply new AI techniques for solving the Light-up puzzle faster and more computationally efficient. The algorithms explored for producing optimal solutions include hill climbing, simulated annealing, feed-forward neural network (FNN), and convolutional neural network (CNN). Two algorithms were developed for hill climb"},"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":"2107.10429","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2021-07-22T03:03:57Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"fed7aafb7dc48b4ae819f1b1aad4587d308fc8dedbaf101a81f9b90a01998080","abstract_canon_sha256":"d15165f89f4685db9e558ff9abb905920893c9758262a7e20c6b8f40e11a3ef3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:59:59.918419Z","signature_b64":"1RTi/B7bz85taSHc2IJIALwsVTrwJPP0A1uNvLZi8ZzqhxWflt2j4t/O/E3WiynPUesjMpy1ngqCWsZ49FYVCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3184fbe89500082ebc59ce3e4e26f09cf50ab582bc8f695748ed20360d96c10","last_reissued_at":"2026-07-05T02:59:59.917930Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:59:59.917930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Shedding some light on Light Up with Artificial Intelligence","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.AI","authors_text":"James Browning, Libo Sun, Roberto Perera","submitted_at":"2021-07-22T03:03:57Z","abstract_excerpt":"The Light-Up puzzle, also known as the AKARI puzzle, has never been solved using modern artificial intelligence (AI) methods. Currently, the most widely used computational technique to autonomously develop solutions involve evolution theory algorithms. This project is an effort to apply new AI techniques for solving the Light-up puzzle faster and more computationally efficient. The algorithms explored for producing optimal solutions include hill climbing, simulated annealing, feed-forward neural network (FNN), and convolutional neural network (CNN). Two algorithms were developed for hill climb"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.10429","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/2107.10429/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":"2107.10429","created_at":"2026-07-05T02:59:59.917987+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.10429v1","created_at":"2026-07-05T02:59:59.917987+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.10429","created_at":"2026-07-05T02:59:59.917987+00:00"},{"alias_kind":"pith_short_12","alias_value":"YMME7PUJKAAI","created_at":"2026-07-05T02:59:59.917987+00:00"},{"alias_kind":"pith_short_16","alias_value":"YMME7PUJKAAIF26F","created_at":"2026-07-05T02:59:59.917987+00:00"},{"alias_kind":"pith_short_8","alias_value":"YMME7PUJ","created_at":"2026-07-05T02:59:59.917987+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/YMME7PUJKAAIF26FTTR6JYTPBH","json":"https://pith.science/pith/YMME7PUJKAAIF26FTTR6JYTPBH.json","graph_json":"https://pith.science/api/pith-number/YMME7PUJKAAIF26FTTR6JYTPBH/graph.json","events_json":"https://pith.science/api/pith-number/YMME7PUJKAAIF26FTTR6JYTPBH/events.json","paper":"https://pith.science/paper/YMME7PUJ"},"agent_actions":{"view_html":"https://pith.science/pith/YMME7PUJKAAIF26FTTR6JYTPBH","download_json":"https://pith.science/pith/YMME7PUJKAAIF26FTTR6JYTPBH.json","view_paper":"https://pith.science/paper/YMME7PUJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.10429&json=true","fetch_graph":"https://pith.science/api/pith-number/YMME7PUJKAAIF26FTTR6JYTPBH/graph.json","fetch_events":"https://pith.science/api/pith-number/YMME7PUJKAAIF26FTTR6JYTPBH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YMME7PUJKAAIF26FTTR6JYTPBH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YMME7PUJKAAIF26FTTR6JYTPBH/action/storage_attestation","attest_author":"https://pith.science/pith/YMME7PUJKAAIF26FTTR6JYTPBH/action/author_attestation","sign_citation":"https://pith.science/pith/YMME7PUJKAAIF26FTTR6JYTPBH/action/citation_signature","submit_replication":"https://pith.science/pith/YMME7PUJKAAIF26FTTR6JYTPBH/action/replication_record"}},"created_at":"2026-07-05T02:59:59.917987+00:00","updated_at":"2026-07-05T02:59:59.917987+00:00"}