{"id":"W1549466950","doi":"10.1609/aaai.v24i1.7754","title":"Simultaneous Elicitation of Preference Features and Utility","year":2010,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Regret; Preference elicitation; Computer science; Heuristic; Set (abstract data type); Preference; Expected utility hypothesis; Focus (optics); Minimax; Feature (linguistics); Machine learning; Artificial intelligence; Utility theory; Mathematical optimization; Mathematics; Mathematical economics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004440156,0.0001547446,0.0002004674,0.00008173982,0.0001330327,0.0001246399,0.001145537,0.00008796985,0.00002822034],"category_scores_gemma":[0.001313376,0.0001105005,0.00005565282,0.0003480222,0.0003186569,0.0002197348,0.0002606865,0.0004725467,0.000007515971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005012871,"about_ca_system_score_gemma":0.0000513118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009251945,"about_ca_topic_score_gemma":0.00002358001,"domain_scores_codex":[0.9987113,0.00001697364,0.000353175,0.0003677054,0.000356284,0.0001945467],"domain_scores_gemma":[0.998525,0.0002424373,0.0003313732,0.0002596358,0.0005742789,0.00006732983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002871204,0.00008180444,0.0005869829,0.00004554178,0.000006375773,1.890834e-7,0.001644307,0.00003028482,0.1011631,0.5679729,0.00002624673,0.3284136],"study_design_scores_gemma":[0.0000286578,0.0002155444,0.003715,0.0001061187,0.000009113357,0.00001058105,0.0002234388,0.2847931,0.5478815,0.1627413,0.00009158757,0.0001839872],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733028,0.00002030212,0.01592689,0.00193579,0.0004434029,0.0002895907,0.000005102648,0.00008255801,0.00799361],"genre_scores_gemma":[0.9914392,0.00001455956,0.008276897,0.00005325062,0.00003731697,0.000005896327,2.482767e-7,0.000005451052,0.0001671792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4467184,"threshold_uncertainty_score":0.450608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04359839882423548,"score_gpt":0.2900635005181569,"score_spread":0.2464651016939215,"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."}}