{"id":"W2902175002","doi":"10.1609/aiide.v14i1.13035","title":"Modelling Player Understanding of Non-Player Character Paths","year":2018,"lang":"en","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":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Backtracking; Abstraction; Path (computing); Representation (politics); Character (mathematics); Adversary; Human–computer interaction; Simple (philosophy); Work (physics); Artificial intelligence; Theoretical computer science; Computer security; Algorithm; Mathematics; Programming language","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":[0.0008170845,0.0008061685,0.0003012338,0.0007823391,0.0002891283,0.001972027,0.001177987,0.001218893,0.004828118],"category_scores_gemma":[0.007066244,0.0006282709,0.000761651,0.0004507596,0.0007911047,0.003830189,0.001493132,0.001368173,0.0006334689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000765711,"about_ca_system_score_gemma":0.0009237654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008568978,"about_ca_topic_score_gemma":0.01292556,"domain_scores_codex":[0.9993266,0.000221253,0.00004300845,0.0001994526,0.000141729,0.00006797251],"domain_scores_gemma":[0.9974992,0.001644984,0.0002361658,0.0002889951,0.0002288818,0.0001017468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006058115,0.0002304472,0.02602684,0.0007828861,0.0001616485,0.00102229,0.00929796,0.7418862,0.01781719,0.09306204,0.002204637,0.1069021],"study_design_scores_gemma":[0.00001822711,0.00008331272,0.002936281,0.00005120804,0.00003623997,0.0001234551,0.000449691,0.9715689,0.002143899,0.01833401,0.004226461,0.00002818725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1530692,0.0001289776,0.8324677,0.000255559,0.00001839746,0.000196171,0.001156959,0.0009512733,0.01175587],"genre_scores_gemma":[0.8529372,0.0001690335,0.1418854,0.00004220702,0.000005227927,0.0001778798,0.001082466,0.00013544,0.003565137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008568978,"threshold_uncertainty_score":0.01703817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08910228045014464,"score_gpt":0.2975196434999934,"score_spread":0.2084173630498488,"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."}}