{"id":"W33532729","doi":"10.1609/aiide.v3i1.18798","title":"A Demonstration of ScriptEase Motivational Ambient and Latent Behaviors for Computer RPGs","year":2007,"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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Scripting language; Guard (computer science); Computer science; Human–computer interaction; Behavior change; Animal behavior; Mechanism (biology); Generative grammar; Psychology; Artificial intelligence; Social psychology; 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":[0.0003556014,0.0002163404,0.0002415843,0.0001616025,0.00009916812,0.0002766054,0.0005016889,0.00006153793,0.000007204943],"category_scores_gemma":[0.0001450401,0.0001675095,0.0001134913,0.0001824047,0.0003586015,0.0009627382,0.0003009342,0.0001430085,0.000003017554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006300688,"about_ca_system_score_gemma":0.00003439931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002238076,"about_ca_topic_score_gemma":0.000006606368,"domain_scores_codex":[0.9982035,0.000007753019,0.0007064526,0.0004368812,0.0003773272,0.0002680954],"domain_scores_gemma":[0.9984661,0.0001987429,0.0004406139,0.0001412614,0.0006502849,0.0001029509],"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":[0.0004120191,0.0007257317,0.007597798,0.00006244636,0.00005859603,7.341562e-7,0.004150129,0.00004715938,0.06687048,0.6299285,0.00006257359,0.2900838],"study_design_scores_gemma":[0.00006234253,0.00107491,0.003436019,0.0003527657,0.00001900371,0.000008662293,0.002117882,0.04414727,0.9138007,0.03467893,0.0000667338,0.0002347673],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7235553,0.000009556256,0.2740521,0.000790991,0.0002563353,0.0005699762,0.00001800107,0.00001983582,0.0007279795],"genre_scores_gemma":[0.9972841,0.00001256698,0.002414758,0.0001406963,0.00003830773,0.00002688344,0.00000234639,0.000008664698,0.00007165637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8469302,"threshold_uncertainty_score":0.6830838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04693422229450893,"score_gpt":0.2950233241174653,"score_spread":0.2480891018229563,"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."}}