{"id":"W2149308876","doi":"10.1109/icinfa.2009.5205101","title":"Implementing a no-loss state in the game of Tic-Tac-Toe using a customized Decision Tree Algorithm","year":2009,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Minimax; Computer science; Game tree; Adversary; State (computer science); Mathematical optimization; Tree (set theory); Decision tree; Game theory; Sequential game; Algorithm; Mathematical economics; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001582079,0.0001461842,0.0002449799,0.0001762072,0.00008269912,0.0001603479,0.001159816,0.00003746045,0.00003981949],"category_scores_gemma":[0.0001771574,0.00009915433,0.0000945041,0.0006760269,0.00005278301,0.0004730754,0.0002294652,0.0001354409,0.0000453949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005006406,"about_ca_system_score_gemma":0.00006938132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005167572,"about_ca_topic_score_gemma":0.0002365891,"domain_scores_codex":[0.9979535,0.0001098356,0.0006441707,0.0003261744,0.0004695625,0.0004967311],"domain_scores_gemma":[0.9985712,0.0004782576,0.0001834654,0.0005762147,0.0001471499,0.00004376462],"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":[0.00002160567,0.00009572487,0.0002627267,0.000002168534,0.000006115335,0.00002875208,0.003594839,0.0007389465,0.002950878,0.002931524,0.00008192232,0.9892848],"study_design_scores_gemma":[0.0003280511,0.0001129678,0.0008258774,0.00004617476,0.000006158556,0.00001623114,0.0003440062,0.9362226,0.01776036,0.04351903,0.0006461947,0.0001723695],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2725161,0.00002839934,0.7259626,0.0001817266,0.00009952131,0.0002110648,0.000001061289,0.00004131851,0.0009582179],"genre_scores_gemma":[0.7107428,0.000009603082,0.2887731,0.0003625073,0.00003363589,0.000003523517,3.712477e-7,0.000005006225,0.00006954942],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9891124,"threshold_uncertainty_score":0.4043396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0361210051492268,"score_gpt":0.3356410775052973,"score_spread":0.2995200723560705,"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."}}