{"id":"W4416014001","doi":"10.1609/aiide.v21i1.36826","title":"From Unstable to Playable: Stabilizing Angry Birds Levels via Object Segmentation","year":2025,"lang":"","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Object (grammar); Stability (learning theory); Image segmentation; Scale-space segmentation; Range (aeronautics); Segmentation-based object categorization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004382653,0.0009756873,0.0005208364,0.0009012707,0.0005909799,0.001812253,0.001179678,0.000808236,0.002421646],"category_scores_gemma":[0.001996375,0.0005704446,0.0006507905,0.0003365956,0.0009736819,0.0009053918,0.001640367,0.0007188555,0.0009269984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006806825,"about_ca_system_score_gemma":0.0004951335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003134022,"about_ca_topic_score_gemma":0.004842378,"domain_scores_codex":[0.999629,0.00004262403,0.00001825391,0.0001078277,0.0001380363,0.00006429655],"domain_scores_gemma":[0.9990323,0.0003433695,0.0001225287,0.0002477935,0.0001636169,0.00009040302],"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":[0.0009278078,0.0002761419,0.01188505,0.0002633758,0.0001279785,0.001524221,0.002590795,0.251837,0.3262073,0.02166423,0.00650737,0.3761888],"study_design_scores_gemma":[0.00003398162,0.0001970286,0.0028715,0.00004745352,0.00006623751,0.0004782901,0.0004531741,0.8923439,0.08233555,0.01253053,0.008588112,0.00005420401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1510094,0.0001404391,0.8402081,0.0001061525,0.00006106367,0.0001102763,0.0001109531,0.003842,0.004411585],"genre_scores_gemma":[0.6687945,0.0000931653,0.3237301,0.0001354694,0.00001764328,0.0000966704,0.0003518561,0.001348001,0.005432737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003134022,"threshold_uncertainty_score":0.008101225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05138709381582503,"score_gpt":0.3162434175093751,"score_spread":0.2648563236935501,"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."}}