{"id":"W90402225","doi":"10.1007/978-3-642-30353-1_2","title":"Macro Learning in Planning as Parameter Configuration","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Macro; Computer science; Domain (mathematical analysis); Set (abstract data type); Task (project management); Planner; Scheme (mathematics); Artificial intelligence; Theoretical computer science; Mathematical optimization; Machine learning; Algorithm; Programming language; 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":[0.0008320509,0.0005429364,0.0006507452,0.0004334252,0.0003440706,0.001171161,0.001252041,0.0006899938,0.009302096],"category_scores_gemma":[0.003938437,0.0005188749,0.0004052876,0.0009760713,0.001469464,0.003028031,0.001395078,0.00170777,0.0009722438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009002154,"about_ca_system_score_gemma":0.0006633331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00282184,"about_ca_topic_score_gemma":0.003214069,"domain_scores_codex":[0.999584,0.000168256,0.00002069015,0.00007871154,0.0001127472,0.00003555021],"domain_scores_gemma":[0.9986339,0.0009771592,0.0000503885,0.0001998343,0.00007954046,0.00005914193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001192052,0.00005453893,0.000564134,0.0001375051,0.00002840625,0.00005922273,0.0001625282,0.3031334,0.001414499,0.4609062,0.003889563,0.2295307],"study_design_scores_gemma":[0.00001205673,0.00003296711,0.0001827925,0.00002635262,0.000008409559,0.00002392038,0.00002844945,0.5506257,0.001253246,0.4439789,0.003816128,0.00001108365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01139002,0.0006490992,0.9665986,0.0003491427,0.00006266037,0.00002442746,0.0000579016,0.000451653,0.02041647],"genre_scores_gemma":[0.6075558,0.001575347,0.3637893,0.0001867246,0.0001588476,0.0002388932,0.0002460708,0.0003404315,0.02590861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009302096,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02225879861836087,"score_gpt":0.2604515108487879,"score_spread":0.2381927122304271,"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."}}