{"id":"W1489599888","doi":"10.1007/978-3-540-89454-4_32","title":"Improvisation and Performance as Models for Interacting with Stories","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Improvisation; Narrative; Computer science; Metaphor; Storytelling; Interactive storytelling; Performing arts; Cognitive science; Human–computer interaction; Agency (philosophy); Computational creativity; Epistemology; Visual arts; Art; Psychology; Creativity; Linguistics; Literature; Social psychology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005571864,0.0003766961,0.0003258912,0.0004125489,0.000329338,0.0006603507,0.001257026,0.0001690482,0.000002076166],"category_scores_gemma":[0.00008061543,0.0003169243,0.00004560133,0.0002443078,0.0005838855,0.001691629,0.0005041784,0.0004048988,0.000005192398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000224711,"about_ca_system_score_gemma":0.0003881981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009119996,"about_ca_topic_score_gemma":0.0001529959,"domain_scores_codex":[0.997462,0.00001132698,0.0003839653,0.00110216,0.0005408445,0.0004996489],"domain_scores_gemma":[0.9979843,0.0006665577,0.0002922573,0.0006318911,0.0003382695,0.00008674913],"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":[0.0000205877,0.00001018726,0.00007039453,0.00005242424,0.0000055873,0.00000707486,0.001844504,0.1656368,0.00006521486,0.02218278,0.00001224801,0.8100922],"study_design_scores_gemma":[0.00006617482,0.0004156271,0.00002024007,0.0003063431,0.000005202468,0.00004343334,8.828783e-7,0.8783304,0.004350478,0.1157266,0.0003550758,0.000379461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002201886,0.0001375796,0.9938821,0.0003635462,0.0009100101,0.0004969581,0.000002062883,0.000113185,0.001892698],"genre_scores_gemma":[0.6576664,0.00001709272,0.3412706,0.0003338497,0.0002946498,0.00002160496,0.000002776104,0.00002641137,0.0003666341],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8097127,"threshold_uncertainty_score":0.9999283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02712593654362648,"score_gpt":0.26654602980446,"score_spread":0.2394200932608335,"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."}}