{"id":"W1526187220","doi":"10.1007/978-0-387-35706-5_15","title":"Search and Knowledge in Lines of Action","year":2004,"lang":"en","type":"book-chapter","venue":"Advances in Computer Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Olympiad; Action (physics); Domain (mathematical analysis); Gold medal; Computer science; Class (philosophy); Position (finance); Artificial intelligence; Mathematics education; Psychology; Mathematics; Art; Business; Art history","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":[0.0003787067,0.0005013303,0.0004427884,0.0009269305,0.000788119,0.00378606,0.0007710158,0.001404743,0.01787649],"category_scores_gemma":[0.002414794,0.0003262812,0.000380546,0.001852408,0.002945033,0.008491211,0.0007767722,0.001439314,0.00227497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001681515,"about_ca_system_score_gemma":0.0008833367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002352194,"about_ca_topic_score_gemma":0.002434184,"domain_scores_codex":[0.9996545,0.0001251487,0.00002230173,0.00006460596,0.0001014328,0.00003198215],"domain_scores_gemma":[0.9991822,0.0005266289,0.00005598092,0.00009675762,0.0001011632,0.00003738513],"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":[0.000008043086,0.000007380518,0.00007123894,0.00005798178,0.000004216328,0.00002647071,0.0003677905,0.001140626,0.0001081656,0.9637269,0.005491341,0.0289899],"study_design_scores_gemma":[0.000005149617,0.000006780467,0.00008573901,0.00007193968,0.000004523938,0.00005825186,0.0001653978,0.002716432,0.0001786023,0.9349425,0.06175931,0.000005311042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.01268222,0.01982213,0.1946806,0.005171708,0.0004365958,0.00004470855,0.0002442386,0.0002820488,0.7666359],"genre_scores_gemma":[0.5084698,0.01795718,0.07982905,0.0007192451,0.0005820337,0.0002497518,0.0005757281,0.000246439,0.3913708],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01787649,"threshold_uncertainty_score":0.05980289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0518174757458234,"score_gpt":0.3466774546907443,"score_spread":0.2948599789449209,"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."}}