{"id":"W1485442455","doi":"10.48550/arxiv.1205.2647","title":"Generating Optimal Plans in Highly-Dynamic Domains","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Plan (archaeology); Process (computing); State (computer science); Adaptation (eye); Tree (set theory); Order (exchange); Mathematical optimization; Algorithm; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003456817,0.0001340043,0.0001267989,0.0001717649,0.0001693309,0.00005015279,0.0005747835,0.00008007709,0.00001291752],"category_scores_gemma":[0.00001541525,0.0001507067,0.00004841343,0.0005604219,0.00003228557,0.0007649229,0.000184153,0.0002067242,0.00008666387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001079716,"about_ca_system_score_gemma":0.00004679091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008161451,"about_ca_topic_score_gemma":0.00004952541,"domain_scores_codex":[0.9989086,0.00009889225,0.0001205299,0.0003369985,0.00005655224,0.0004784272],"domain_scores_gemma":[0.999292,0.0001196327,0.00006606658,0.0003588541,0.00002137411,0.0001420709],"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":[0.00001721464,0.0001084249,0.1417412,0.00001744156,0.00002349724,0.0002811957,0.001765624,0.7254373,0.0009781278,0.1282907,0.0002539382,0.001085316],"study_design_scores_gemma":[0.0004216283,0.00003820589,0.00555649,0.00002920183,0.000007437028,0.00001185721,0.00009563677,0.9923782,0.0001453878,0.000491009,0.0005723986,0.0002525009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.58191,0.00004515988,0.4168592,0.00004540684,0.0001427913,0.00004299308,0.000002518201,0.00009710424,0.0008549023],"genre_scores_gemma":[0.9842987,0.00001035315,0.01497458,0.0001114847,0.00004535088,3.10477e-7,0.000005474999,0.0000078553,0.0005458837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4023888,"threshold_uncertainty_score":0.6145642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03620234738660056,"score_gpt":0.1769605089965621,"score_spread":0.1407581616099615,"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."}}