{"id":"W25763866","doi":"10.1371/journal.pone.0115657","title":"Regret-based utility elicitation in constraint-based decision problems","year":2005,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Toronto","funders":"","keywords":"Regret; Preference elicitation; Mathematical optimization; Computer science; Preference; Heuristic; Constraint (computer-aided design); Minimax; Binary decision diagram; Optimization problem; Product (mathematics); Artificial intelligence; Machine learning; Mathematics; Theoretical computer science","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006891488,0.0002563807,0.000221778,0.0006448349,0.0001089805,0.0003840632,0.0007297554,0.0001321856,0.001204183],"category_scores_gemma":[0.0005467143,0.0002612934,0.0001136295,0.0005104582,0.0002079016,0.0006045604,0.00004985746,0.000336008,0.0004643339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002676599,"about_ca_system_score_gemma":0.0003958298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007204872,"about_ca_topic_score_gemma":0.0007772833,"domain_scores_codex":[0.9972034,0.0001239002,0.0009202197,0.0006941757,0.0007407755,0.0003175193],"domain_scores_gemma":[0.9982501,0.000342146,0.0002673289,0.0004512287,0.0005577967,0.0001314038],"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":[0.00004995949,0.0002191441,0.0004491184,0.000004080511,0.000004769136,0.000006337623,0.0001586703,0.07582363,0.0009781494,0.2468519,0.00003742821,0.6754168],"study_design_scores_gemma":[0.0001575299,0.00008523933,0.002578448,0.0001895856,0.000002099013,0.000004246266,0.00008774938,0.9500656,0.02095854,0.02496977,0.0006378036,0.0002634025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009845957,0.000006176235,0.9667054,0.01429523,0.0007074375,0.0003751332,0.00001411672,0.000189925,0.007860587],"genre_scores_gemma":[0.9209086,0.00000820427,0.07760141,0.001303223,0.00006914775,0.00004571666,0.00002574788,0.000009876737,0.00002805528],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9110627,"threshold_uncertainty_score":0.9999839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1005012886310663,"score_gpt":0.3222350740173838,"score_spread":0.2217337853863176,"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."}}