{"id":"W2747118187","doi":"10.1016/j.artint.2017.05.004","title":"MM: A bidirectional search algorithm that is guaranteed to meet in the middle","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Science Foundation of Sri Lanka; Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation","keywords":"Incremental heuristic search; Bidirectional search; Beam search; Heuristic; Best-first search; Search algorithm; Algorithm; Computer science; Disjoint sets; Node (physics); Search problem; Depth-first search; Iterative deepening depth-first search; Null-move heuristic; 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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002083552,0.0001639766,0.0001837835,0.000281236,0.0007357533,0.001339925,0.003672909,0.00006810561,0.0002228585],"category_scores_gemma":[0.0006161369,0.0001287785,0.00007463485,0.0007070611,0.0001931245,0.0004613886,0.0005772788,0.0002362593,0.0007999309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005589284,"about_ca_system_score_gemma":0.0001463932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000801578,"about_ca_topic_score_gemma":0.0002689199,"domain_scores_codex":[0.9971876,0.000248088,0.0003607073,0.0005975965,0.00107941,0.0005266719],"domain_scores_gemma":[0.9976963,0.0003532955,0.00008573457,0.001484352,0.0002407317,0.0001395457],"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":[0.00002920851,0.000363583,0.0005586339,0.00001362713,0.00003090124,0.0001337419,0.01354572,0.002430582,0.0003328074,0.1785863,0.002970337,0.8010045],"study_design_scores_gemma":[0.00004133646,0.00009605529,0.0009173176,0.00003231498,0.000003559592,0.00002778662,0.0006478056,0.9402025,0.02684878,0.02798004,0.002954981,0.0002474858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002879347,0.00004150385,0.9801928,0.01123407,0.0005919303,0.0004713835,0.0000123763,0.00005455123,0.004522062],"genre_scores_gemma":[0.7651695,0.00004574263,0.2305206,0.002741026,0.0003252587,0.0001336079,0.000002260894,0.0000241084,0.001037869],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.937772,"threshold_uncertainty_score":0.9999781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2313199868634884,"score_gpt":0.3798248892331862,"score_spread":0.1485049023696978,"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."}}