{"id":"W1989564919","doi":"10.1109/iros.2005.1545099","title":"Reducing spatial interference in robot teams by local-investment aggression","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Aggression; Task (project management); Computer science; Investment (military); Interference (communication); Competition (biology); Work (physics); Human–computer interaction; Artificial intelligence; Collision avoidance; Computer vision; Simulation; Computer security; Engineering; Psychology; Collision; Social psychology; Telecommunications","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":[0.001931333,0.0005553481,0.0009381241,0.0007251826,0.0005684819,0.0007657843,0.001377857,0.0004244411,0.0006984283],"category_scores_gemma":[0.00766508,0.0003776541,0.0004962315,0.0004575183,0.0008903591,0.001223685,0.001628617,0.0006177697,0.0001725564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000785325,"about_ca_system_score_gemma":0.0008159544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001608788,"about_ca_topic_score_gemma":0.002543364,"domain_scores_codex":[0.9983768,0.0007027365,0.00009468193,0.0001779899,0.000479997,0.0001676691],"domain_scores_gemma":[0.9950369,0.001996748,0.001369238,0.000689158,0.0006211159,0.0002869267],"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":[0.0004911484,0.0003555423,0.02728973,0.0001754468,0.0002715217,0.0002619567,0.001186843,0.6915284,0.02974497,0.01619156,0.0007681882,0.2317347],"study_design_scores_gemma":[0.0000292288,0.0003106425,0.00520897,0.00001249557,0.00004075396,0.0001334781,0.0001346364,0.9828382,0.00666382,0.003864027,0.0007292094,0.00003458338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2166728,0.00008925927,0.7804498,0.0001012078,0.0000155466,0.00006490623,0.00001631683,0.0006047033,0.001985492],"genre_scores_gemma":[0.8829325,0.00003283895,0.1162832,0.00003528469,0.000009999434,0.00007275253,0.00002171595,0.00004616963,0.0005654823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001931333,"threshold_uncertainty_score":0.01021403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01115595693521408,"score_gpt":0.2833514556690504,"score_spread":0.2721954987338363,"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."}}