{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000763177,0.0006888118,0.0006430096,0.001097258,0.0003961665,0.0008368296,0.0007518905,0.0007451323,0.0009066944],"category_scores_gemma":[0.005654177,0.0008034644,0.0006741327,0.0008339607,0.0007553168,0.001858598,0.001004729,0.001162785,0.0002128589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006855011,"about_ca_system_score_gemma":0.0009998388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01149577,"about_ca_topic_score_gemma":0.01587589,"domain_scores_codex":[0.9997166,0.00008384364,0.00001909605,0.00009433475,0.00006245919,0.00002371335],"domain_scores_gemma":[0.9974875,0.001680329,0.0003148043,0.0003295878,0.0001153594,0.00007246287],"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":[0.00007781372,0.00003530535,0.006976824,0.00006936073,0.00007283771,0.0001635125,0.0001472272,0.9153374,0.002389879,0.01834947,0.0006598805,0.05572039],"study_design_scores_gemma":[0.000004472556,0.000006047871,0.0008138437,0.000005404747,0.000008055078,0.00001621553,0.00001602847,0.9816428,0.0004662799,0.01670531,0.0003099958,0.000005603329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06319579,0.00009492964,0.9354039,0.0001332952,0.00001058299,0.00001987806,0.0002373183,0.0004591993,0.0004450479],"genre_scores_gemma":[0.7915857,0.000374783,0.2062794,0.00005010899,0.00002653797,0.00005193672,0.0006869886,0.00007799578,0.0008665322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01149577,"threshold_uncertainty_score":0.02285773,"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."}}