{"id":"W172987266","doi":"","title":"Generating Domain-Specific Planners through Automatic Parameter Configuration in LPG","year":2011,"lang":"en","type":"article","venue":"University of Huddersfield Repository (University of Huddersfield)","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Planner; Domain (mathematical analysis); Set (abstract data type); Parametric statistics; Computer science; Range (aeronautics); Mathematical optimization; Scale (ratio); Artificial intelligence; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001227006,0.0009279037,0.0008677085,0.0008311752,0.0004808703,0.0009429052,0.00163688,0.001065892,0.006508083],"category_scores_gemma":[0.005320844,0.0006769381,0.0007254999,0.0009251318,0.001577874,0.001318653,0.002152659,0.001336554,0.001557397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006688543,"about_ca_system_score_gemma":0.001193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001664205,"about_ca_topic_score_gemma":0.003553392,"domain_scores_codex":[0.9992612,0.0002766647,0.00003746621,0.0001896959,0.00015798,0.00007692276],"domain_scores_gemma":[0.9986743,0.0007868383,0.00008702213,0.0002968779,0.0001180783,0.00003692547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001993567,0.00009405195,0.001019112,0.0002670277,0.00003470983,0.0002415549,0.0002750511,0.7734136,0.008325135,0.02589025,0.007172124,0.1830681],"study_design_scores_gemma":[0.00005764198,0.00005347882,0.0001452407,0.00002151185,0.00001190289,0.00005652509,0.0000805753,0.9676787,0.005963491,0.02157427,0.004339916,0.00001670471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02715502,0.0001054624,0.9571853,0.0001298268,0.00002857941,0.0002400072,0.0003200402,0.008949144,0.005886685],"genre_scores_gemma":[0.2945891,0.0001124243,0.7007672,0.0001218002,0.00001647978,0.0006396416,0.0009689589,0.001059265,0.001725084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006508083,"threshold_uncertainty_score":0.02177173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02471241361564298,"score_gpt":0.1774740103866368,"score_spread":0.1527615967709938,"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."}}