{"id":"W1586168340","doi":"10.1007/978-3-540-72665-4_20","title":"Multiagent Constraint Satisfaction with Multiply Sectioned Constraint Networks","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Backtracking; Constraint satisfaction problem; Computer science; Constraint satisfaction; Breakout; Local consistency; Constraint graph; Bounded function; Constraint satisfaction dual problem; Graph; Mathematical optimization; Constraint (computer-aided design); Theoretical computer science; Algorithm; Artificial intelligence; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009770577,0.0007127016,0.0005690046,0.001143079,0.0004679085,0.0006258619,0.0009654524,0.0005087031,0.0001416569],"category_scores_gemma":[0.00006692881,0.0006473578,0.0001484851,0.0007906416,0.001552904,0.0007243541,0.0003719662,0.001234273,0.0000307072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007189517,"about_ca_system_score_gemma":0.0007506428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001153833,"about_ca_topic_score_gemma":0.002088875,"domain_scores_codex":[0.9955627,0.00005166283,0.0007699778,0.001706086,0.001096233,0.0008133594],"domain_scores_gemma":[0.997102,0.0005071255,0.0005146067,0.001052139,0.0004921492,0.0003319805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001427829,0.00001611522,0.0005608806,0.00001159325,0.00002150756,0.00008572655,0.0002566118,0.3230373,0.0000320826,0.008330688,0.000004649284,0.6676286],"study_design_scores_gemma":[0.0009285084,0.0002446472,0.003602429,0.0003425349,0.0000185461,0.0006210721,0.000001828293,0.9886149,0.000229074,0.004070848,0.0003542322,0.0009714052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001052284,0.00009900453,0.9929656,0.0003641367,0.002260494,0.0008199274,0.000007107416,0.0004087623,0.002969681],"genre_scores_gemma":[0.5260998,0.00005826679,0.4722837,0.001063972,0.0003338184,0.00001233449,0.0000126632,0.0000405457,0.00009490834],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6666572,"threshold_uncertainty_score":0.9995978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01739351589251973,"score_gpt":0.2394741181908174,"score_spread":0.2220806022982977,"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."}}