{"id":"W2604635684","doi":"10.1609/aaai.v31i1.11031","title":"Logical Filtering and Smoothing: State Estimation in Partially Observable Domains","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Smoothing; Observable; State (computer science); Computer science; State space; Algorithm; Logical framework; Estimation; Dynamical systems theory; Dynamical system (definition); Theoretical computer science; Mathematics; Computer vision","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":[0.005541612,0.001053396,0.001285011,0.001627056,0.001159134,0.002628653,0.00284177,0.001665501,0.002451239],"category_scores_gemma":[0.02610843,0.0009719465,0.001839682,0.001684355,0.003203887,0.00667217,0.002826476,0.002894944,0.0003900249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002040822,"about_ca_system_score_gemma":0.002889264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00886863,"about_ca_topic_score_gemma":0.007505862,"domain_scores_codex":[0.9964961,0.001223328,0.0002735146,0.0008131771,0.000980298,0.0002136477],"domain_scores_gemma":[0.9794516,0.01527599,0.00130823,0.002676432,0.001012853,0.0002749613],"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":[0.0004067453,0.0001090407,0.00262492,0.0003273143,0.0001477689,0.0002312663,0.0006116318,0.5272977,0.004552051,0.2973436,0.001984487,0.1643636],"study_design_scores_gemma":[0.00002444561,0.00003265658,0.0002341685,0.00003244923,0.00002916502,0.00004583434,0.00004360342,0.8329971,0.002887263,0.1619474,0.001699575,0.00002644572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004612211,0.000114209,0.9942294,0.0001568196,0.00001546025,0.00002258405,0.000037315,0.0003518609,0.0004601149],"genre_scores_gemma":[0.3591329,0.0004377341,0.6381346,0.0002068021,0.0000928838,0.0001648772,0.0003172841,0.0001503275,0.001362548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00886863,"threshold_uncertainty_score":0.02930719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1056481588798765,"score_gpt":0.311098857837069,"score_spread":0.2054506989571925,"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."}}