{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002017,0.001074854,0.001088918,0.000650025,0.0004792628,0.001350524,0.002013564,0.0008357617,0.005822388],"category_scores_gemma":[0.005240992,0.000593416,0.001258678,0.0008369664,0.001398493,0.002578448,0.001887481,0.002057509,0.001018679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009165839,"about_ca_system_score_gemma":0.002011912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00232901,"about_ca_topic_score_gemma":0.003655691,"domain_scores_codex":[0.9976909,0.0009171049,0.00012071,0.000496848,0.000545048,0.0002293664],"domain_scores_gemma":[0.996838,0.001535427,0.0002966266,0.0009953107,0.0002547676,0.00007994149],"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":[0.00072816,0.0004071875,0.0009605874,0.0002008139,0.00007167687,0.0001543825,0.0002375588,0.6842359,0.0222788,0.07624624,0.004169221,0.2103095],"study_design_scores_gemma":[0.00009818198,0.0001978666,0.0001820383,0.00001698841,0.00002190358,0.00003362152,0.00002482855,0.9618663,0.009386942,0.0249698,0.003176491,0.00002504067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02508186,0.0002090616,0.9670838,0.0002198314,0.00003561062,0.000113479,0.00008799755,0.00299454,0.004173787],"genre_scores_gemma":[0.3639207,0.000149066,0.6311264,0.0002635196,0.00003607384,0.0003255901,0.0004789294,0.0007031835,0.002996435],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005822388,"threshold_uncertainty_score":0.01947784,"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."}}