{"id":"W4206919836","doi":"10.1109/ssci50451.2021.9659974","title":"An approximate dynamic programming approach to tackling mass evacuation operations","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Armed Forces; Defence Research and Development Canada","funders":"","keywords":"Markov decision process; Dynamic programming; Computer science; Bellman equation; Curse of dimensionality; Mathematical optimization; Operations research; Stochastic programming; Markov process; Representation (politics); Artificial intelligence; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002793963,0.0003387877,0.0002676252,0.000225392,0.0003382656,0.0005347456,0.0003087704,0.0001347325,0.0001106979],"category_scores_gemma":[0.00004184359,0.0003920801,0.00009941048,0.000898793,0.00005781879,0.0006806719,0.00003040686,0.0002806752,0.0001961305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002632802,"about_ca_system_score_gemma":0.0001631854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000596473,"about_ca_topic_score_gemma":0.00005599675,"domain_scores_codex":[0.9977617,0.00009939324,0.0005763131,0.0005963455,0.000558981,0.0004073152],"domain_scores_gemma":[0.9986781,0.00008111069,0.00004753438,0.0004005203,0.0005579669,0.00023479],"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":[0.0000154688,0.0001451221,0.00001481523,0.00006228753,0.00003910932,0.000006803131,0.001052277,0.9638524,0.0126162,0.01465843,0.00004632897,0.007490736],"study_design_scores_gemma":[0.00008442783,0.000111204,0.00005519784,0.00004885864,0.00002121595,0.00002815075,0.001214552,0.986432,0.009362471,0.001801903,0.0004015965,0.0004384278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01714439,0.00005216007,0.977573,0.0005386959,0.0008083269,0.0004724425,0.0000558414,0.0003563786,0.00299873],"genre_scores_gemma":[0.7205368,0.00006820403,0.277128,0.0002734609,0.0001587849,0.000198919,0.00104488,0.00007660302,0.0005142555],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7033924,"threshold_uncertainty_score":0.9998531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01480358829325944,"score_gpt":0.2750570176054727,"score_spread":0.2602534293122132,"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."}}