{"id":"W3045478170","doi":"10.1155/2020/1360491","title":"A Generalized Dynamic Potential Energy Model for Multiagent Path Planning","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Bottleneck; Energy (signal processing); Computer science; Multi-agent system; Potential field; Dijkstra's algorithm; Field (mathematics); Potential energy; Motion planning; Construct (python library); Agent-based model; Dissipation; Pedestrian; Simulation; Efficient energy use; Artificial intelligence; Shortest path problem; Transport engineering; Engineering; Theoretical computer science; Mathematics; Robot","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005569997,0.0009582667,0.0007550612,0.0007183279,0.0005618141,0.001137092,0.002062548,0.00120737,0.003326752],"category_scores_gemma":[0.001604795,0.0004586203,0.0009892074,0.001013167,0.0007701119,0.002417003,0.001015508,0.001167004,0.0004371942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001268677,"about_ca_system_score_gemma":0.001181126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01188569,"about_ca_topic_score_gemma":0.005419081,"domain_scores_codex":[0.9996014,0.0001212511,0.00002183335,0.00009536497,0.000106731,0.00005338385],"domain_scores_gemma":[0.9995789,0.0001949874,0.00005031214,0.00003029842,0.0001076726,0.00003784454],"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":[0.000009939658,0.000009958283,0.0001891664,0.00002805949,0.00001128337,0.0000505291,0.00003166215,0.9653324,0.0003447986,0.02842982,0.0003842539,0.005178101],"study_design_scores_gemma":[0.000002556779,0.000007505361,0.0000410037,0.000002831458,0.000003106019,0.0000108351,0.000006965827,0.9920707,0.00004172065,0.007373726,0.000434628,0.000004369575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009656919,0.0003519335,0.9832841,0.0002566533,0.00006079992,0.00004252825,0.0001003013,0.0001055517,0.006141204],"genre_scores_gemma":[0.8356346,0.001217113,0.1470112,0.0001788768,0.00007267731,0.0005319663,0.0003952149,0.0001201745,0.01483821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01188569,"threshold_uncertainty_score":0.023633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01214626662440894,"score_gpt":0.2468232193133623,"score_spread":0.2346769526889534,"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."}}