{"id":"W2402290736","doi":"10.1609/socs.v3i1.18259","title":"A* Variants for Optimal Multi-Agent Pathfinding","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation","keywords":"Pathfinding; Computer science; Theoretical computer science; Shortest path problem","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":[0.002001838,0.001093636,0.0008244884,0.001450462,0.0009719557,0.001539513,0.003446369,0.001697246,0.008762071],"category_scores_gemma":[0.006030152,0.0006295014,0.001647235,0.002177093,0.001133151,0.002807861,0.002763202,0.002407994,0.001800562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007530836,"about_ca_system_score_gemma":0.001279156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002100539,"about_ca_topic_score_gemma":0.002451793,"domain_scores_codex":[0.9985701,0.0004599334,0.0001535997,0.0002888998,0.0003919878,0.0001354737],"domain_scores_gemma":[0.9961339,0.001857136,0.0002952643,0.0009879064,0.0005445107,0.0001814372],"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":[0.0004808336,0.0004297142,0.001021625,0.0005113241,0.0001729119,0.0002635633,0.0002142143,0.2886663,0.008866867,0.3337664,0.02024067,0.3453656],"study_design_scores_gemma":[0.0001193485,0.00032894,0.0003188744,0.00005688712,0.0000561697,0.0003638976,0.00007859472,0.7894111,0.004788013,0.180657,0.02377393,0.00004725896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01244855,0.0004730635,0.9768515,0.0003039456,0.0001753884,0.0001567499,0.0002840839,0.000917916,0.008388766],"genre_scores_gemma":[0.1280351,0.0004660666,0.8641468,0.0002783706,0.0001046048,0.0006255432,0.0007199378,0.0003308331,0.005292785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008762071,"threshold_uncertainty_score":0.02931201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02463526732612297,"score_gpt":0.2993281981942744,"score_spread":0.2746929308681514,"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."}}