{"id":"W2401554262","doi":"10.1609/icaps.v25i1.13704","title":"Understanding and Improving Local Exploration for GBFS","year":2015,"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":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Maxima and minima; Satisficing; Local search (optimization); Heuristic; Mathematics; Mathematical optimization; Algorithm; Computer science; Artificial intelligence","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":[0.001244285,0.001022582,0.0009328044,0.000805682,0.0007409803,0.001229624,0.001221369,0.0008261446,0.004130099],"category_scores_gemma":[0.006327174,0.00052143,0.0008412444,0.0009652117,0.001654367,0.002721406,0.002338866,0.001768854,0.0005874912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001253528,"about_ca_system_score_gemma":0.002216368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007950091,"about_ca_topic_score_gemma":0.009831643,"domain_scores_codex":[0.9989758,0.0003378669,0.0000391291,0.0001773901,0.0003168791,0.0001529789],"domain_scores_gemma":[0.9980848,0.001259549,0.0001073457,0.0003129796,0.000168961,0.00006639679],"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":[0.0001455397,0.00007011519,0.001267442,0.0002490807,0.0000331196,0.00009539312,0.0003256504,0.7841231,0.003746018,0.04695265,0.003652246,0.1593396],"study_design_scores_gemma":[0.00002575382,0.00005231834,0.0002470638,0.00003343428,0.00001704961,0.00003938194,0.00008306389,0.9461133,0.00248444,0.04720271,0.003687413,0.00001399013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04873975,0.001389785,0.9377747,0.0006302603,0.00005530121,0.000077362,0.0001025577,0.002449384,0.008781046],"genre_scores_gemma":[0.5245824,0.0008099619,0.469947,0.0001946266,0.00005113671,0.0001948818,0.0002665393,0.0005665278,0.003386987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007950091,"threshold_uncertainty_score":0.01580763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2048224908150635,"score_gpt":0.3098547891615983,"score_spread":0.1050322983465348,"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."}}