{"id":"W1573576464","doi":"10.1007/3-540-44914-0_18","title":"Experiments with Automatically Created Memory-Based Heuristics","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Heuristics; Heuristic; Computer science; Set (abstract data type); Lookup table; Table (database); State (computer science); Conjecture; Space (punctuation); Algorithm; Theoretical computer science; Artificial intelligence; Mathematics; Data mining; Programming language; Discrete 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":[0.002982441,0.001763322,0.00144454,0.0008748036,0.0006359093,0.001587136,0.003875161,0.002475446,0.009647606],"category_scores_gemma":[0.04466337,0.001228872,0.0005869477,0.001349409,0.001041463,0.003185006,0.001276031,0.002210882,0.001986442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001476,"about_ca_system_score_gemma":0.0009916447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006900851,"about_ca_topic_score_gemma":0.00369025,"domain_scores_codex":[0.997847,0.0009417805,0.0002959442,0.0004283591,0.0003163955,0.0001705537],"domain_scores_gemma":[0.9241939,0.0672711,0.001166985,0.004214212,0.002400868,0.0007530093],"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":[0.04166374,0.0276257,0.006055929,0.005476539,0.0009707721,0.001144562,0.004292552,0.3651699,0.04937829,0.005688474,0.01626771,0.4762658],"study_design_scores_gemma":[0.00868026,0.01232592,0.005341061,0.0003675226,0.0009777771,0.0005320337,0.001351614,0.8802572,0.07009092,0.009762816,0.01002489,0.0002880329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9501998,0.001584204,0.03097865,0.0003893983,0.0004827505,0.0006251564,0.001497143,0.003216503,0.01102627],"genre_scores_gemma":[0.9358093,0.0006152472,0.05437034,0.0001936393,0.00006583763,0.0006214811,0.002571313,0.0007731047,0.004979671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009647606,"threshold_uncertainty_score":0.03227448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479963355955197,"score_gpt":0.2387250828678451,"score_spread":0.2239254493082932,"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."}}