{"id":"W3116419616","doi":"10.22215/etd/2019-13509","title":"Procedural Generation of Three-Dimensional Game Levels with Interior Architecture","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Modular design; Computer science; Architecture; Polygon mesh; Fitness function; Function (biology); Game engine; Engineering drawing; Genetic algorithm; Engineering; Human–computer interaction; Programming language; Computer graphics (images); Machine learning","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":[0.00009785206,0.0002630895,0.0003120245,0.0001783659,0.00003781199,0.00008828011,0.0007109576,0.0001829412,0.0001379231],"category_scores_gemma":[0.00003479455,0.000187918,0.0000841696,0.00022491,0.00004444634,0.0002513826,0.00006575584,0.000250843,0.00007917753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003364803,"about_ca_system_score_gemma":0.0004083687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001011712,"about_ca_topic_score_gemma":0.002497014,"domain_scores_codex":[0.998374,0.00002559552,0.0003927451,0.0005036096,0.000495423,0.0002085972],"domain_scores_gemma":[0.9987397,0.00005063844,0.000305423,0.0004820388,0.0003745769,0.00004759735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004701696,0.0002695243,0.0008449425,0.0009883948,0.0004078217,0.00003306562,0.01614225,0.02232939,0.4597706,0.05705399,0.003047457,0.4386424],"study_design_scores_gemma":[0.0001614912,0.0007541683,0.002752983,0.0006463743,0.00004111947,0.00004538707,0.0002477338,0.2125182,0.7777448,0.003863748,0.0003519558,0.0008720289],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5911589,0.00009514524,0.404823,0.0002631688,0.001038152,0.0005837739,0.000007391258,0.0001091679,0.001921283],"genre_scores_gemma":[0.9495726,9.185911e-7,0.04187427,0.000177542,0.0001167474,0.00002851824,0.00009531884,0.00002668968,0.008107388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4377703,"threshold_uncertainty_score":0.7663074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04404814416702867,"score_gpt":0.2888084240081574,"score_spread":0.2447602798411287,"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."}}