{"id":"W4312258528","doi":"10.1609/icaps.v23i1.13543","title":"Better Time Constrained Search via Randomization and Postprocessing","year":2013,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Satisficing; Computer science; Bounding overwatch; Set (abstract data type); Mathematical optimization; Beam search; Metric (unit); Heuristic; Quality (philosophy); Iterative deepening depth-first search; Ranking (information retrieval); Hypersphere; Incremental heuristic search; Search algorithm; Algorithm; Machine learning; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004435785,0.0001471284,0.0001720882,0.0001445299,0.0002199316,0.0005964388,0.0004986353,0.00007717706,0.00002152343],"category_scores_gemma":[0.0001038272,0.0001106418,0.00002844685,0.0001499964,0.0000898185,0.0005390051,0.0001822312,0.0002285112,0.00000849741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000160562,"about_ca_system_score_gemma":0.00004465743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002375169,"about_ca_topic_score_gemma":5.28072e-8,"domain_scores_codex":[0.9989321,0.00001155597,0.0002596408,0.0002871498,0.0003211861,0.0001884305],"domain_scores_gemma":[0.9991577,0.0001116181,0.0001928674,0.0000627775,0.0004084181,0.00006664734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003846844,0.0001419071,0.09752543,0.000479227,0.0004157514,0.000008333709,0.01270306,0.007722658,0.7641514,0.06735955,0.001439395,0.04766858],"study_design_scores_gemma":[0.0006405513,0.00004137292,0.002590169,0.0006574258,0.000006964477,0.00003800583,0.0001235156,0.9832132,0.01021062,0.002337094,0.000006567252,0.0001344614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832001,0.00006591911,0.007145275,0.004829396,0.0001190249,0.0001784213,0.000002325452,0.0003026369,0.004156919],"genre_scores_gemma":[0.9761178,0.000004249097,0.02343032,0.0003069028,0.00002923016,0.000008122445,0.000003682144,0.000007906197,0.00009176795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9754906,"threshold_uncertainty_score":0.5751473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01847997360293742,"score_gpt":0.2526648700279775,"score_spread":0.2341848964250401,"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."}}