{"id":"W4312621041","doi":"10.1609/icaps.v32i1.19805","title":"Beam Search: Faster and Monotonic","year":2022,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Science Foundation","keywords":"Beam search; Satisficing; Beam (structure); Computation; Monotonic function; Heuristic; Mathematical optimization; Beam diameter; Computer science; Search algorithm; Mathematics; Algorithm; Physics; Optics; Artificial intelligence; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005696455,0.001851258,0.002402924,0.00212353,0.001010407,0.002688888,0.002870779,0.002571339,0.0146995],"category_scores_gemma":[0.01875958,0.001349513,0.002027377,0.002672032,0.001651927,0.005551975,0.003021389,0.004351318,0.00434266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001525883,"about_ca_system_score_gemma":0.002946925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007497427,"about_ca_topic_score_gemma":0.007512536,"domain_scores_codex":[0.996172,0.001400948,0.0001783125,0.0005488764,0.001410698,0.0002891136],"domain_scores_gemma":[0.9923394,0.004325844,0.0004363576,0.001457175,0.001255567,0.0001856269],"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":[0.0005313485,0.0002269815,0.001534107,0.000561649,0.0002376097,0.0001507282,0.0003792328,0.3028927,0.008479758,0.1651318,0.03168786,0.4881864],"study_design_scores_gemma":[0.0002552847,0.0001580905,0.0003418031,0.0002321753,0.00007358639,0.0001733779,0.00008852003,0.8610642,0.003572377,0.1061461,0.02783892,0.00005563732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007139287,0.002003346,0.9772887,0.0006966484,0.0002186824,0.0001605958,0.0001960251,0.002009704,0.01028702],"genre_scores_gemma":[0.08035788,0.001366324,0.9054767,0.001461721,0.0002791132,0.0006197559,0.0005560587,0.001487266,0.008395161],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0146995,"threshold_uncertainty_score":0.04917467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06016796766122735,"score_gpt":0.3081002708569113,"score_spread":0.2479323031956839,"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."}}