{"id":"W2121441589","doi":"10.1609/aimag.v30i1.2174","title":"Agents, Bodies, Constraints, Dynamics, and Evolution","year":2009,"lang":"en","type":"article","venue":"AI Magazine","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Theme (computing); Constraint (computer-aided design); Dynamics (music); Computer science; Artificial intelligence; Constraint satisfaction problem; Association (psychology); Intelligent agent; Management science; Cognitive science; Engineering; Epistemology; Psychology; World Wide Web; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008868437,0.00008987488,0.00008704851,0.00005681923,0.00008370575,0.00009436587,0.0001225129,0.00004299408,0.000079412],"category_scores_gemma":[0.00002737949,0.00009232985,0.00002191133,0.0001461106,0.00007754492,0.0003767027,0.00003851465,0.00007077859,0.00006690367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009371712,"about_ca_system_score_gemma":0.00004176746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006113842,"about_ca_topic_score_gemma":0.00006044746,"domain_scores_codex":[0.9993541,0.0000231482,0.0001393445,0.0002230741,0.0001111945,0.0001491619],"domain_scores_gemma":[0.9996164,0.00001218987,0.00004458637,0.0001926278,0.0000589794,0.00007519505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000002325029,0.0000229495,0.007418971,0.000003134594,0.00000589067,0.000007494881,0.00005336657,0.00009478755,0.00004921509,0.5406433,0.004815817,0.4468827],"study_design_scores_gemma":[0.0004893278,0.00007793123,0.5836368,0.00001364535,0.00000699199,0.0001032417,0.00001453943,0.3957025,0.00001172671,0.017134,0.00261775,0.0001915224],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001265859,0.00010361,0.9743379,0.01039276,0.0001930655,0.00009933001,0.000007479608,0.000185705,0.01341433],"genre_scores_gemma":[0.9777042,0.00005129081,0.01977072,0.001856543,0.00002490208,0.000001435354,0.00001460345,0.000002320054,0.0005739523],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9764384,"threshold_uncertainty_score":0.3765102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008000819366642754,"score_gpt":0.2331126536950781,"score_spread":0.2251118343284353,"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."}}