{"id":"W2196640769","doi":"10.1609/aaai.v29i1.9641","title":"A Generalization of Sleep Sets Based on Operator Sequence Redundancy","year":2015,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; Universität Basel; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Correctness; Redundancy (engineering); Computer science; Operator (biology); Pruning; Commutative property; Sequence (biology); Generalization; Algorithm; Set (abstract data type); Sleep (system call); Mathematics; Theoretical computer science; Artificial intelligence; Discrete mathematics; Programming language","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001965474,0.0007744159,0.001104481,0.001784299,0.00120731,0.001172988,0.00244226,0.0008394571,0.00272163],"category_scores_gemma":[0.007920158,0.0005775174,0.001711685,0.001309942,0.002103655,0.002780485,0.002432871,0.002011346,0.0005020339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007998736,"about_ca_system_score_gemma":0.001893803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001532139,"about_ca_topic_score_gemma":0.0029316,"domain_scores_codex":[0.9979377,0.0004339724,0.0002037045,0.0004230364,0.0008053512,0.0001961976],"domain_scores_gemma":[0.9926245,0.003107057,0.0006843042,0.002534395,0.0008316343,0.0002181818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007545245,0.0004275349,0.004998757,0.0008220692,0.0002602364,0.0007937871,0.001291626,0.1184407,0.05495296,0.3741443,0.009480285,0.4336331],"study_design_scores_gemma":[0.000174024,0.001064718,0.002797151,0.0001767441,0.0002665107,0.001226899,0.0002492192,0.5302188,0.04532621,0.3855419,0.03280564,0.0001521402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07722038,0.0003948958,0.9131945,0.0003399384,0.00009626939,0.0003461043,0.0004691138,0.002170969,0.005767772],"genre_scores_gemma":[0.4340163,0.0003061146,0.5592005,0.0004566604,0.0001014269,0.0006449134,0.0007734114,0.0005155628,0.003985053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00272163,"threshold_uncertainty_score":0.01039457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1375997925046119,"score_gpt":0.3166891713952504,"score_spread":0.1790893788906386,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}