{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:VD73X7ADDRBLOOVMX5MMZ3UZQD","short_pith_number":"pith:VD73X7AD","schema_version":"1.0","canonical_sha256":"a8ffbbfc031c42b73aacbf58ccee9980d936e615cdfd938760a1253bc0744e3f","source":{"kind":"arxiv","id":"1411.3895","version":1},"attestation_state":"computed","paper":{"title":"Learning Fuzzy Controllers in Mobile Robotics with Embedded Preprocessing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"A. Bugar\\'in, I. Rodr\\'iguez-Fdez, M. Mucientes","submitted_at":"2014-11-14T13:11:32Z","abstract_excerpt":"The automatic design of controllers for mobile robots usually requires two stages. In the first stage,sensorial data are preprocessed or transformed into high level and meaningful values of variables whichare usually defined from expert knowledge. In the second stage, a machine learning technique is applied toobtain a controller that maps these high level variables to the control commands that are actually sent tothe robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learningstage in order to get controllers directly starting from sensorial raw data"},"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":"1411.3895","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2014-11-14T13:11:32Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"1aff3baa1965467db7ba053bd98e951fc507025251fcc376d5c27cf11a62b028","abstract_canon_sha256":"b0e2b3068b2df3c0c5cbc11b95e53981b585d2bd508c5b49088b75ae41702760"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:37:37.409062Z","signature_b64":"b45gRFNCx7mfOtRHKsBwdkk5iCIQtJTpadVDHpx3fqLslr+bb+90fyQn3V6y4jjUTch02JPCmb+KUmVMPyQjDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8ffbbfc031c42b73aacbf58ccee9980d936e615cdfd938760a1253bc0744e3f","last_reissued_at":"2026-05-18T02:37:37.408600Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:37:37.408600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Fuzzy Controllers in Mobile Robotics with Embedded Preprocessing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"A. Bugar\\'in, I. Rodr\\'iguez-Fdez, M. Mucientes","submitted_at":"2014-11-14T13:11:32Z","abstract_excerpt":"The automatic design of controllers for mobile robots usually requires two stages. In the first stage,sensorial data are preprocessed or transformed into high level and meaningful values of variables whichare usually defined from expert knowledge. In the second stage, a machine learning technique is applied toobtain a controller that maps these high level variables to the control commands that are actually sent tothe robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learningstage in order to get controllers directly starting from sensorial raw data"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.3895","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":"1411.3895","created_at":"2026-05-18T02:37:37.408673+00:00"},{"alias_kind":"arxiv_version","alias_value":"1411.3895v1","created_at":"2026-05-18T02:37:37.408673+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.3895","created_at":"2026-05-18T02:37:37.408673+00:00"},{"alias_kind":"pith_short_12","alias_value":"VD73X7ADDRBL","created_at":"2026-05-18T12:28:52.271510+00:00"},{"alias_kind":"pith_short_16","alias_value":"VD73X7ADDRBLOOVM","created_at":"2026-05-18T12:28:52.271510+00:00"},{"alias_kind":"pith_short_8","alias_value":"VD73X7AD","created_at":"2026-05-18T12:28:52.271510+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/VD73X7ADDRBLOOVMX5MMZ3UZQD","json":"https://pith.science/pith/VD73X7ADDRBLOOVMX5MMZ3UZQD.json","graph_json":"https://pith.science/api/pith-number/VD73X7ADDRBLOOVMX5MMZ3UZQD/graph.json","events_json":"https://pith.science/api/pith-number/VD73X7ADDRBLOOVMX5MMZ3UZQD/events.json","paper":"https://pith.science/paper/VD73X7AD"},"agent_actions":{"view_html":"https://pith.science/pith/VD73X7ADDRBLOOVMX5MMZ3UZQD","download_json":"https://pith.science/pith/VD73X7ADDRBLOOVMX5MMZ3UZQD.json","view_paper":"https://pith.science/paper/VD73X7AD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1411.3895&json=true","fetch_graph":"https://pith.science/api/pith-number/VD73X7ADDRBLOOVMX5MMZ3UZQD/graph.json","fetch_events":"https://pith.science/api/pith-number/VD73X7ADDRBLOOVMX5MMZ3UZQD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VD73X7ADDRBLOOVMX5MMZ3UZQD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VD73X7ADDRBLOOVMX5MMZ3UZQD/action/storage_attestation","attest_author":"https://pith.science/pith/VD73X7ADDRBLOOVMX5MMZ3UZQD/action/author_attestation","sign_citation":"https://pith.science/pith/VD73X7ADDRBLOOVMX5MMZ3UZQD/action/citation_signature","submit_replication":"https://pith.science/pith/VD73X7ADDRBLOOVMX5MMZ3UZQD/action/replication_record"}},"created_at":"2026-05-18T02:37:37.408673+00:00","updated_at":"2026-05-18T02:37:37.408673+00:00"}