{"id":"W2098052437","doi":"10.1109/crv.2012.62","title":"Socially-Driven Collective Path Planning for Robot Missions","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Motion planning; Planner; Robot; Parameterized complexity; Path (computing); Computer science; Mathematical optimization; Control (management); Selection (genetic algorithm); Operations research; Simulation; Artificial intelligence; Engineering; Computer network; Mathematics; Algorithm","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":[0.0002971349,0.0001384918,0.0001778056,0.00008086647,0.0003766391,0.00008073098,0.0005292014,0.0000846648,0.00001210617],"category_scores_gemma":[0.0001454221,0.0001201532,0.00007888657,0.0002813727,0.00002506723,0.0004249071,0.0001564148,0.0001089643,0.00004656434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001091333,"about_ca_system_score_gemma":0.0002748705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000090423,"about_ca_topic_score_gemma":1.988128e-7,"domain_scores_codex":[0.9987569,0.00005216559,0.0001721333,0.0002600114,0.000204247,0.0005544719],"domain_scores_gemma":[0.9989833,0.000358046,0.00007928738,0.0002764571,0.00008237935,0.0002205892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008718413,0.001409668,0.06706853,0.0001027889,0.0005056751,0.00007204234,0.09356476,0.06463304,0.008313586,0.2638367,0.4540185,0.04638758],"study_design_scores_gemma":[0.001391685,0.000404101,0.04043461,0.0001125666,0.00003633489,0.00005444855,0.0006413741,0.9353593,0.001975892,0.007640847,0.01107044,0.0008784318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004057888,0.000094884,0.9866823,0.0007297429,0.0007696569,0.0002898443,0.000004346461,0.0002951783,0.01072825],"genre_scores_gemma":[0.07993812,7.22728e-7,0.9156874,0.0003583546,0.000287459,0.00006008251,0.000004472988,0.00001340175,0.003649986],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8707262,"threshold_uncertainty_score":0.4899705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06186934041744652,"score_gpt":0.3181825458990875,"score_spread":0.2563132054816409,"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."}}