{"id":"W2910547178","doi":"10.1609/icaps.v29i1.3505","title":"Robust and Adaptive Planning under Model Uncertainty","year":2019,"lang":"en","type":"preprint","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency","keywords":"Robustness (evolution); Computation; Mathematical optimization; Computer science; Adversary; Monte Carlo method; Tree (set theory); Decision tree; Monte Carlo tree search; Bayesian probability; Artificial intelligence; Algorithm; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001771012,0.0003756681,0.0005558572,0.0005156202,0.0002332016,0.0008746769,0.001702132,0.0003259618,0.00003024462],"category_scores_gemma":[0.001722484,0.0002601492,0.0001095131,0.0002317903,0.0002739812,0.0003208224,0.002000742,0.001314822,0.000006951303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000119127,"about_ca_system_score_gemma":0.0002808471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002841643,"about_ca_topic_score_gemma":8.473223e-7,"domain_scores_codex":[0.9958515,0.00003181469,0.000721667,0.000977456,0.002076061,0.0003415234],"domain_scores_gemma":[0.9964334,0.0006517818,0.0009179128,0.0002420289,0.001631269,0.0001235484],"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":[0.000179017,0.00002031551,0.002640448,0.00003996111,0.00009441901,0.000001558049,0.0009844503,0.9884625,0.0007223932,0.005942116,0.0002748157,0.0006379877],"study_design_scores_gemma":[0.0003334275,0.00005092643,0.002217888,0.00142245,0.00001768622,0.0000134259,0.00339,0.9371794,0.0005065016,0.05459757,0.0000106824,0.0002600867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661191,0.0003816692,0.02076922,0.002182487,0.0007524072,0.0005156384,0.0001415271,0.0002425404,0.008895442],"genre_scores_gemma":[0.9884351,0.00004102605,0.01050155,0.0001221728,0.00007739352,0.00002008627,0.000009729501,0.00002656993,0.0007663322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05128316,"threshold_uncertainty_score":0.9999851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2897143656682048,"score_gpt":0.4215858200438864,"score_spread":0.1318714543756816,"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."}}