{"id":"W2156622941","doi":"10.7939/r3fm9h","title":"Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement - Extended Version","year":2010,"lang":"en","type":"article","venue":"University of Alberta Library","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Satisficing; Plan (archaeology); Computer science; Quality (philosophy); Operations research; Graph; Mathematical optimization; Mathematics; Artificial intelligence; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002071762,0.001653564,0.001361824,0.001852599,0.0008446327,0.001377125,0.003265634,0.001811196,0.01439466],"category_scores_gemma":[0.00683668,0.0008208671,0.002079414,0.001873031,0.001699409,0.002565171,0.00268027,0.003203459,0.002732989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001675466,"about_ca_system_score_gemma":0.003676706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01409867,"about_ca_topic_score_gemma":0.01724492,"domain_scores_codex":[0.9973672,0.0006478047,0.000155922,0.0005563782,0.0009784629,0.0002943134],"domain_scores_gemma":[0.9964457,0.001787433,0.0002832543,0.0008984934,0.0004844462,0.0001006192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006207098,0.0005695329,0.001430853,0.0004246335,0.0001062078,0.0001489238,0.0002838895,0.1870435,0.004144203,0.03282263,0.0220947,0.7503102],"study_design_scores_gemma":[0.0003189938,0.0003193202,0.0008967586,0.00006863708,0.0001037457,0.00020435,0.00007682173,0.9273871,0.005767551,0.03564635,0.02915209,0.00005835572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009923181,0.0005412515,0.9694658,0.0005854638,0.0001390895,0.0005249926,0.0004967814,0.008964523,0.009358943],"genre_scores_gemma":[0.0744589,0.000267591,0.9144616,0.0003129801,0.00008748349,0.0005926095,0.001400265,0.0009425304,0.007476041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01439466,"threshold_uncertainty_score":0.04815495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02018128664592779,"score_gpt":0.2202260040205725,"score_spread":0.2000447173746447,"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."}}