{"id":"W4382866761","doi":"10.1609/socs.v16i1.27299","title":"Hybrid Search with Graph Neural Networks for Constraint-Based Navigation Planning [Extended Abstract]","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Computer science; Artificial neural network; Constraint (computer-aided design); Graph; Overhead (engineering); Vehicle routing problem; Routing (electronic design automation); Inference; Artificial intelligence; Task (project management); Constraint programming; Route planning; Mathematical optimization; Machine learning; Theoretical computer science; Engineering; Computer network; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009973107,0.0001690518,0.0001562303,0.0001977829,0.0002464077,0.0004876718,0.00219164,0.00004565413,0.000004433175],"category_scores_gemma":[0.00004702535,0.0001287582,0.0001093275,0.0005623553,0.0001314732,0.0005800552,0.0004316117,0.0002844419,0.000005143541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008235452,"about_ca_system_score_gemma":0.000049075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002918415,"about_ca_topic_score_gemma":1.995135e-7,"domain_scores_codex":[0.9977065,0.00001063015,0.0002766793,0.0004368531,0.001185822,0.0003835618],"domain_scores_gemma":[0.9987379,0.0002438573,0.0001517632,0.0001809118,0.0006075947,0.00007795587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001085423,0.0005549347,0.006191266,0.000254831,0.000321313,0.00002259082,0.0003973684,0.05549493,0.01168687,0.9041523,0.008574914,0.01126329],"study_design_scores_gemma":[0.002341435,0.0005196356,0.002976899,0.0002428079,0.00001425237,0.000007368831,0.00008394174,0.9485594,0.0342337,0.010118,0.0006395211,0.0002630581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9556564,0.000008324505,0.01774986,0.01340862,0.006483017,0.00164535,0.00007888642,0.000429162,0.004540401],"genre_scores_gemma":[0.9985905,0.00000237995,0.0007128004,0.000102009,0.0003390587,0.00006359928,0.00005971437,0.00001888744,0.0001110979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8940343,"threshold_uncertainty_score":0.5250609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02342475220236735,"score_gpt":0.2782809439319044,"score_spread":0.2548561917295371,"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."}}