{"id":"W2964370931","doi":"10.1002/cav.1898","title":"Coupling agent motivations and spatial behaviors for authoring multiagent narratives","year":2019,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; National Science Foundation","keywords":"Computer science; Narrative; Human–computer interaction; Resource (disambiguation); Task (project management); Multi-agent system; Coupling (piping); Artificial intelligence; Systems engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008568195,0.0001067457,0.0001079278,0.00008184289,0.0000990952,0.00008245178,0.00003661592,0.00004045774,0.00003250943],"category_scores_gemma":[0.000003858008,0.000111526,0.00002494471,0.00006445675,0.00001579946,0.0001410636,0.00002640868,0.00006437387,0.000008019636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002602717,"about_ca_system_score_gemma":0.000005379273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002006858,"about_ca_topic_score_gemma":0.00001479096,"domain_scores_codex":[0.9994982,0.000006806103,0.000171034,0.0001415538,0.00007572916,0.0001066921],"domain_scores_gemma":[0.9997624,0.0000474261,0.00002631663,0.00006837557,0.00003766579,0.00005778356],"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":[0.00006160585,0.000182148,0.042362,0.0004991629,0.0001865937,0.000001939829,0.02647121,0.6847354,0.0392319,0.05158904,0.001873289,0.1528058],"study_design_scores_gemma":[0.0004161185,0.0000644789,0.05224146,0.00002804405,0.000007538466,0.00000108727,0.0002609729,0.9457916,0.0001711877,0.00004493519,0.0008435332,0.0001290497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5362565,0.0000276422,0.4631043,0.00003992514,0.000235586,0.0001822674,0.00000561462,0.00008254373,0.00006558205],"genre_scores_gemma":[0.9925793,0.00001708756,0.006997039,0.00004434713,0.00007919401,0.00002089712,0.00004061188,0.00001659996,0.0002049115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4563228,"threshold_uncertainty_score":0.4547899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552511397009129,"score_gpt":0.2564157404654286,"score_spread":0.2408906264953374,"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."}}