{"id":"W4387394760","doi":"10.1609/aiide.v19i1.27532","title":"Synthesizing Priority Planning Formulae for Multi-Agent Pathfinding","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Pathfinding; Set (abstract data type); Context (archaeology); Function (biology); Fitness function; Readability; Space (punctuation); Domain (mathematical analysis); Artificial intelligence; Mathematical optimization; Theoretical computer science; Machine learning; Genetic algorithm; Mathematics; Programming language","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":[0.001272361,0.0008333085,0.000488229,0.001046484,0.0004832727,0.000868998,0.001356103,0.0009315397,0.00274104],"category_scores_gemma":[0.008521554,0.0004087038,0.0008635486,0.0008011143,0.001179867,0.001305758,0.0009882932,0.001246725,0.000443924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040639,"about_ca_system_score_gemma":0.002048194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00252199,"about_ca_topic_score_gemma":0.004374325,"domain_scores_codex":[0.9993487,0.0001592904,0.00006797828,0.0001268284,0.0002409895,0.00005615007],"domain_scores_gemma":[0.9971806,0.00197025,0.0001940401,0.0002590945,0.0003440182,0.00005203645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000946703,0.0001618087,0.001605736,0.0004818227,0.00004373448,0.0002315521,0.0003551115,0.6021349,0.0201733,0.07479192,0.002420734,0.2975048],"study_design_scores_gemma":[0.00004359692,0.00006799175,0.0001330776,0.00004383451,0.00002150547,0.00005491969,0.00005023059,0.943227,0.01034566,0.04213272,0.003865687,0.0000138952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01700881,0.00007868547,0.9791726,0.0001264,0.00003051413,0.00009702358,0.0000955682,0.001251794,0.002138633],"genre_scores_gemma":[0.1563914,0.0001013989,0.8417996,0.00007398614,0.00001316531,0.0002162874,0.0002494667,0.0002803997,0.0008743321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00274104,"threshold_uncertainty_score":0.009169698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1357745462913799,"score_gpt":0.3430046838827316,"score_spread":0.2072301375913518,"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."}}