{"id":"W2967535898","doi":"10.1109/cec.2019.8790055","title":"Applying an Adaptive Generative Representation to the Investigation of Affordances in Puzzles","year":2019,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Affordance; Computer science; Representation (politics); Adaptability; Solver; Simple (philosophy); Generative grammar; Artificial intelligence; Variety (cybernetics); ENCODE; Machine learning; Human–computer interaction; Theoretical computer science; Programming language","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":[0.0002989971,0.00005702199,0.00008162346,0.0000792996,0.00003411638,0.00005160736,0.0004545689,0.00001995998,0.00001195722],"category_scores_gemma":[0.00004020396,0.00003941324,0.00001587975,0.0004762299,0.00003890024,0.0007063722,0.00009533833,0.00004711583,0.00005155698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002148004,"about_ca_system_score_gemma":0.00003450795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004951027,"about_ca_topic_score_gemma":0.001456432,"domain_scores_codex":[0.9991852,0.0001047578,0.0001967481,0.0002302892,0.0001827791,0.0001002481],"domain_scores_gemma":[0.9993811,0.0001225252,0.00007386696,0.0003159521,0.00007766206,0.00002887745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002537685,0.00003159652,0.05077052,0.000005733936,0.000009617306,0.000001035427,0.03905154,0.1031967,0.05709316,0.5931918,0.0002196068,0.1564034],"study_design_scores_gemma":[0.00002950348,0.0001486026,0.008518936,0.0000189054,9.235361e-7,5.709531e-7,0.005533081,0.4178889,0.5278463,0.03982947,0.00009083703,0.00009399383],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5747123,0.00001278392,0.4224617,0.0008835236,0.0001194995,0.0005128524,3.402773e-7,0.00002264072,0.00127434],"genre_scores_gemma":[0.9187277,0.000002016867,0.08069146,0.0003095405,0.00002521367,0.00008854566,7.000999e-7,0.000002437126,0.0001523598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5533623,"threshold_uncertainty_score":0.1607225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07876375416908163,"score_gpt":0.3234088099228587,"score_spread":0.2446450557537771,"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."}}