{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001646211,0.0006305871,0.0002132896,0.000469665,0.000602431,0.001282935,0.0007746753,0.0006250201,0.002317801],"category_scores_gemma":[0.005743933,0.000441719,0.0003933203,0.0001651656,0.000969818,0.001560429,0.002667976,0.0006915475,0.0002889495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005908129,"about_ca_system_score_gemma":0.0006393004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008197785,"about_ca_topic_score_gemma":0.0017401,"domain_scores_codex":[0.9991974,0.0004317008,0.00006654399,0.0001005113,0.0001569236,0.00004690809],"domain_scores_gemma":[0.9975826,0.001424926,0.0002814088,0.0002797397,0.0001905713,0.0002408057],"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":[0.0003223336,0.0003672996,0.009401136,0.0004221461,0.0001082964,0.001198844,0.008018336,0.5472431,0.06421655,0.252034,0.002588943,0.1140789],"study_design_scores_gemma":[0.00002870707,0.00006258783,0.0004925211,0.00003454923,0.0000223721,0.0001226929,0.0005500313,0.943305,0.01139592,0.03136006,0.01259547,0.00002994289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1011976,0.00007788837,0.8893725,0.0003077623,0.00003833988,0.0001628135,0.00005910231,0.001021328,0.007762746],"genre_scores_gemma":[0.7094449,0.00008030173,0.2872354,0.00004938399,0.00001163375,0.0001630478,0.00008004941,0.0001804554,0.002754766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002317801,"threshold_uncertainty_score":0.008706093,"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."}}