{"id":"W2566659572","doi":"10.1609/aaai.v30i1.10081","title":"On the Completeness of Best-First Search Variants That Use Random Exploration","year":2016,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Incremental heuristic search; Greedy algorithm; Computer science; Robustness (evolution); Completeness (order theory); Best-first search; Heuristic; Beam search; Greedy randomized adaptive search procedure; Mathematical optimization; Search algorithm; Graph; Theoretical computer science; Algorithm; Mathematics; 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":[0.03369268,0.002110952,0.003851816,0.003511899,0.003321127,0.00480116,0.004333892,0.003702024,0.004782738],"category_scores_gemma":[0.1794441,0.001845045,0.003689293,0.004105673,0.008366121,0.01443651,0.006842501,0.006946022,0.001564716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002886017,"about_ca_system_score_gemma":0.006940272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004218962,"about_ca_topic_score_gemma":0.003677432,"domain_scores_codex":[0.9805834,0.009856739,0.00109735,0.002911494,0.004172657,0.001378297],"domain_scores_gemma":[0.7458621,0.1988966,0.007634229,0.03660233,0.008765647,0.002239206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005266767,0.0001793175,0.003338367,0.0004144132,0.0001729833,0.0001686976,0.0009503815,0.4621776,0.001115797,0.4740443,0.004231199,0.05268026],"study_design_scores_gemma":[0.0001037181,0.0001222188,0.0003760785,0.0001755516,0.00006811945,0.0002172585,0.00008321801,0.553368,0.001283293,0.4410299,0.003122051,0.00005059225],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03402697,0.00113852,0.9477627,0.001638388,0.00007453319,0.0001848499,0.0003382297,0.0009874018,0.01384838],"genre_scores_gemma":[0.5206553,0.002145142,0.4668108,0.001167922,0.0002805887,0.0008291783,0.00109114,0.001890716,0.005129179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03369268,"threshold_uncertainty_score":0.1781861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2383701447523257,"score_gpt":0.3007328333075417,"score_spread":0.06236268855521601,"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."}}