{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:BOVZGPKGOFIVPB4JWDADGI2KYE","short_pith_number":"pith:BOVZGPKG","schema_version":"1.0","canonical_sha256":"0bab933d467151578789b0c033234ac10e360c4d96a912d78d0fed7c9a0f7037","source":{"kind":"arxiv","id":"1304.1533","version":1},"attestation_state":"computed","paper":{"title":"Comparing Expert Systems Built Using Different Uncertain Inference Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bruce M. Perrin, David S. Vaughan, Robert M. Yadrick","submitted_at":"2013-03-27T19:40:56Z","abstract_excerpt":"This study compares the inherent intuitiveness or usability of the most prominent methods for managing uncertainty in expert systems, including those of EMYCIN, PROSPECTOR, Dempster-Shafer theory, fuzzy set theory, simplified probability theory (assuming marginal independence), and linear regression using probability estimates. Participants in the study gained experience in a simple, hypothetical problem domain through a series of learning trials.  They were then randomly assigned to develop an expert system using one of the six Uncertain Inference Systems (UISs) listed above. Performance of t"},"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":"1304.1533","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-03-27T19:40:56Z","cross_cats_sorted":[],"title_canon_sha256":"11b4dc007776abd3779e1df1c0704a495cd54fd52185ddc352bc79c3feac1402","abstract_canon_sha256":"6d013e73c2fa16aebddfd0c91b8fa90cbf91fe20cb7f88a9d84882b33c3e5260"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:28:55.465000Z","signature_b64":"lXnXDPXuhb5yG/633GWaOHY4zBsrq3FyNtXT5JSke9oUEyAWyOYLsZz0M3tuuzH14hs6lm4zXXTpkFsJcY8RDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0bab933d467151578789b0c033234ac10e360c4d96a912d78d0fed7c9a0f7037","last_reissued_at":"2026-05-18T03:28:55.464431Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:28:55.464431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comparing Expert Systems Built Using Different Uncertain Inference Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bruce M. Perrin, David S. Vaughan, Robert M. Yadrick","submitted_at":"2013-03-27T19:40:56Z","abstract_excerpt":"This study compares the inherent intuitiveness or usability of the most prominent methods for managing uncertainty in expert systems, including those of EMYCIN, PROSPECTOR, Dempster-Shafer theory, fuzzy set theory, simplified probability theory (assuming marginal independence), and linear regression using probability estimates. Participants in the study gained experience in a simple, hypothetical problem domain through a series of learning trials.  They were then randomly assigned to develop an expert system using one of the six Uncertain Inference Systems (UISs) listed above. Performance of t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1304.1533","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":"1304.1533","created_at":"2026-05-18T03:28:55.464505+00:00"},{"alias_kind":"arxiv_version","alias_value":"1304.1533v1","created_at":"2026-05-18T03:28:55.464505+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1304.1533","created_at":"2026-05-18T03:28:55.464505+00:00"},{"alias_kind":"pith_short_12","alias_value":"BOVZGPKGOFIV","created_at":"2026-05-18T12:27:40.988391+00:00"},{"alias_kind":"pith_short_16","alias_value":"BOVZGPKGOFIVPB4J","created_at":"2026-05-18T12:27:40.988391+00:00"},{"alias_kind":"pith_short_8","alias_value":"BOVZGPKG","created_at":"2026-05-18T12:27:40.988391+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/BOVZGPKGOFIVPB4JWDADGI2KYE","json":"https://pith.science/pith/BOVZGPKGOFIVPB4JWDADGI2KYE.json","graph_json":"https://pith.science/api/pith-number/BOVZGPKGOFIVPB4JWDADGI2KYE/graph.json","events_json":"https://pith.science/api/pith-number/BOVZGPKGOFIVPB4JWDADGI2KYE/events.json","paper":"https://pith.science/paper/BOVZGPKG"},"agent_actions":{"view_html":"https://pith.science/pith/BOVZGPKGOFIVPB4JWDADGI2KYE","download_json":"https://pith.science/pith/BOVZGPKGOFIVPB4JWDADGI2KYE.json","view_paper":"https://pith.science/paper/BOVZGPKG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1304.1533&json=true","fetch_graph":"https://pith.science/api/pith-number/BOVZGPKGOFIVPB4JWDADGI2KYE/graph.json","fetch_events":"https://pith.science/api/pith-number/BOVZGPKGOFIVPB4JWDADGI2KYE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BOVZGPKGOFIVPB4JWDADGI2KYE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BOVZGPKGOFIVPB4JWDADGI2KYE/action/storage_attestation","attest_author":"https://pith.science/pith/BOVZGPKGOFIVPB4JWDADGI2KYE/action/author_attestation","sign_citation":"https://pith.science/pith/BOVZGPKGOFIVPB4JWDADGI2KYE/action/citation_signature","submit_replication":"https://pith.science/pith/BOVZGPKGOFIVPB4JWDADGI2KYE/action/replication_record"}},"created_at":"2026-05-18T03:28:55.464505+00:00","updated_at":"2026-05-18T03:28:55.464505+00:00"}