{"id":"W4399177038","doi":"10.1609/icaps.v34i1.31455","title":"Exact Multi-objective Path Finding with Negative Weights","year":2024,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"State Government of Victoria; Australian Government","keywords":"Mathematical optimization; Shortest path problem; Computer science; Path (computing); Point (geometry); Graph; Enhanced Data Rates for GSM Evolution; Task (project management); Pareto optimal; Algorithm; Multi-objective optimization; Mathematics; Theoretical computer science; Artificial intelligence; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007196763,0.001041084,0.0009131869,0.0007790059,0.0005297347,0.0007138385,0.001255785,0.001215236,0.003660535],"category_scores_gemma":[0.002257159,0.0005523697,0.0006603638,0.001128225,0.0004782262,0.001429061,0.001091323,0.001094959,0.0005051119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006495669,"about_ca_system_score_gemma":0.001181216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003591706,"about_ca_topic_score_gemma":0.004376621,"domain_scores_codex":[0.9996139,0.0001089751,0.00001931684,0.00009652234,0.0001038661,0.00005731776],"domain_scores_gemma":[0.9991403,0.0005291246,0.00009279126,0.00008993174,0.0001009107,0.00004697367],"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":[0.00007031662,0.00007984781,0.0004578219,0.0001087292,0.00003378758,0.00007913129,0.00002747204,0.9175873,0.001436575,0.01050906,0.001526001,0.06808398],"study_design_scores_gemma":[0.00001133165,0.00003091716,0.00007436989,0.000005895332,0.000004954327,0.00002155734,0.000009089774,0.9929732,0.0003907145,0.006039507,0.0004347105,0.000003654496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05959257,0.000368065,0.9317457,0.0002296195,0.0000557602,0.0001048156,0.0001630617,0.0006315788,0.007108771],"genre_scores_gemma":[0.4759185,0.0001824194,0.5193205,0.0001068948,0.00002464993,0.0002016548,0.0002935204,0.0001463469,0.003805551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003660535,"threshold_uncertainty_score":0.01224571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03990089687618361,"score_gpt":0.2981061731035023,"score_spread":0.2582052762273188,"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."}}