{"id":"W96574868","doi":"","title":"Inferring complex agent motions from partial trajectory observations","year":2007,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Alberta","funders":"","keywords":"Computer science; Probabilistic logic; Trajectory; Path (computing); Inference; Heuristic; Graph; Artificial intelligence; Motion planning; Markov chain; Motion (physics); Machine learning; Algorithm; Theoretical computer science; Robot","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005947829,0.0003063954,0.0002565315,0.0003157712,0.0002762859,0.000537892,0.001479938,0.0001333802,0.0006414275],"category_scores_gemma":[0.000251795,0.0003187932,0.0001623256,0.0003775025,0.0001584446,0.0005946023,0.0002584084,0.000445634,0.0008204702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000188669,"about_ca_system_score_gemma":0.0001802329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006293643,"about_ca_topic_score_gemma":0.000535504,"domain_scores_codex":[0.9969272,0.00007917627,0.0009499506,0.0007628169,0.0007782963,0.0005025409],"domain_scores_gemma":[0.9980822,0.0002138349,0.0002436066,0.0006295788,0.0005654215,0.0002653419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001812612,0.0002543797,0.0001768175,0.000002012487,0.00004000002,0.00002820062,0.0007511064,0.003185562,0.02586654,0.8511285,0.0001688219,0.11838],"study_design_scores_gemma":[0.0000783463,0.0001404906,0.01758396,0.0001364627,0.00001289421,0.000009799478,0.0003346763,0.745618,0.05028567,0.1837847,0.001404857,0.0006100753],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04494323,0.00001234633,0.9411419,0.003451359,0.001943239,0.0001743285,0.00003971913,0.0003779168,0.007915975],"genre_scores_gemma":[0.9595144,0.00003123839,0.03874686,0.001070911,0.0004070908,0.00002454641,0.00005725236,0.00001599589,0.0001317253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9145712,"threshold_uncertainty_score":0.9999575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3282678335686683,"score_gpt":0.355972819707533,"score_spread":0.02770498613886468,"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."}}