{"id":"W2073055172","doi":"10.1109/cig.2014.6932890","title":"Using partial satisfaction planning to automatically select NPCs' goals and generate plans in a simulation game","year":2014,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Scripting language; Selection (genetic algorithm); Order (exchange); Artificial intelligence; State (computer science); Motion planning; Control (management); Robot; Algorithm","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":[0.000502794,0.0006541113,0.0003646943,0.0002756129,0.0004018674,0.0005299768,0.0005990755,0.0004205364,0.001985607],"category_scores_gemma":[0.001701532,0.0003212665,0.0003778183,0.0002139818,0.0007167779,0.0005391669,0.0007852307,0.0004938699,0.0002644029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004717195,"about_ca_system_score_gemma":0.001118364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005499418,"about_ca_topic_score_gemma":0.007721745,"domain_scores_codex":[0.9996663,0.0001475735,0.00001912837,0.00005460038,0.00007160751,0.00004070485],"domain_scores_gemma":[0.9993794,0.0004126377,0.00004732778,0.00005302806,0.00005508211,0.00005258188],"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":[0.0004253904,0.0001842031,0.002510894,0.0001566972,0.00005149234,0.0003315031,0.0006890775,0.8340755,0.01496175,0.02866347,0.001750232,0.1161998],"study_design_scores_gemma":[0.00002194448,0.00004950268,0.0001252531,0.000003967883,0.000009342492,0.00002593961,0.00004216716,0.9900788,0.003454997,0.005355289,0.0008266328,0.000006156212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1028362,0.00005465229,0.8901545,0.0001710214,0.00001459423,0.0001551942,0.0000643396,0.001221374,0.005328157],"genre_scores_gemma":[0.739768,0.00005805209,0.2581529,0.00004850146,0.000005024251,0.0001412765,0.0001198231,0.0001115464,0.001594777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005499418,"threshold_uncertainty_score":0.01093483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07215507090733456,"score_gpt":0.3574347841411342,"score_spread":0.2852797132337996,"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."}}