{"id":"W4240822539","doi":"10.22215/etd/2015-10615","title":"Multi-agent planning for interactive emergent narrative systems","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Narrative; Adventure; Computer science; Domain (mathematical analysis); Narrative structure; Human–computer interaction; Artificial intelligence; Art; Literature","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009115653,0.0008139748,0.0005584027,0.0005358554,0.0008688324,0.001653427,0.001166154,0.000970379,0.005296958],"category_scores_gemma":[0.002646187,0.0005343815,0.0008055012,0.0003976882,0.001189301,0.001151824,0.002087486,0.001129603,0.0004963268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001629349,"about_ca_system_score_gemma":0.001580309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00668124,"about_ca_topic_score_gemma":0.007687298,"domain_scores_codex":[0.9996017,0.0001470255,0.0000325792,0.00007455334,0.00009968482,0.00004449092],"domain_scores_gemma":[0.9990355,0.0006810023,0.00007163067,0.00006286144,0.00008110628,0.00006802726],"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":[0.00006279247,0.00004999482,0.0004934799,0.0001565004,0.00005606122,0.0002817784,0.0003116908,0.8509454,0.001597903,0.1251391,0.001335761,0.01956963],"study_design_scores_gemma":[0.0000230414,0.00001595226,0.00004698253,0.00001328814,0.000009330257,0.00002224129,0.00004781488,0.9575092,0.0004435403,0.03801866,0.003843833,0.000006130199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02262529,0.0006457061,0.9555664,0.0004895226,0.0000613438,0.0002232727,0.0001316923,0.0006571248,0.01959956],"genre_scores_gemma":[0.6053847,0.0007726018,0.3806627,0.0001071116,0.000050971,0.0007306951,0.0003344476,0.0001726891,0.01178408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00668124,"threshold_uncertainty_score":0.01772004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1124894205594,"score_gpt":0.4113497945551065,"score_spread":0.2988603739957065,"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."}}