{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001322151,0.0008213863,0.0006972628,0.000547608,0.0007180921,0.001517748,0.001594145,0.001020644,0.004936952],"category_scores_gemma":[0.005469026,0.0008937045,0.0007576863,0.001114425,0.0009611502,0.002305782,0.002146637,0.002383222,0.0004718659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009018677,"about_ca_system_score_gemma":0.0009094744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002195531,"about_ca_topic_score_gemma":0.003466852,"domain_scores_codex":[0.9989275,0.0004311962,0.00005362192,0.0001829221,0.0003297163,0.00007500523],"domain_scores_gemma":[0.9975185,0.001683673,0.000192974,0.0002261219,0.0002812574,0.00009747844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009804707,0.0000663802,0.0003293655,0.0001449255,0.0000890726,0.000207331,0.0001906325,0.7350479,0.003424873,0.1778105,0.003367466,0.07922357],"study_design_scores_gemma":[0.00001518918,0.00001922249,0.00004995805,0.00001396131,0.00001230095,0.00004440066,0.00002538777,0.9174553,0.001284753,0.07823364,0.002837696,0.000008242823],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009585167,0.0001736333,0.9820787,0.000188942,0.00003530203,0.00005417773,0.000044673,0.0001209193,0.007718415],"genre_scores_gemma":[0.314971,0.0004374721,0.6721042,0.0001675127,0.00008720066,0.0003238495,0.0002647359,0.0001142477,0.0115298],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004936952,"threshold_uncertainty_score":0.01651579,"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."}}